{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Likelihood functions in HSSM explained\n",
    "\n",
    "One of the design goals of HSSM is its flexibility. It is built from ground up to support many types of likelihood functions out-of-the-box. For more tailored applications, HSSM provides a convenient toolbox. This allows users to create their own likelihood functions, which can seamlessly integrate with the HSSM class, facilitating a highly customizable analysis environment. This notebook focuses on explaining how to use different types of likelihoods with HSSM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colab Instructions\n",
    "\n",
    "If you would like to run this tutorial on Google colab, please click this [link](https://github.com/lnccbrown/HSSM/blob/main/docs/tutorials/likelihoods.ipynb). \n",
    "\n",
    "Once you are *in the colab*, follow the *installation instructions below* and then **restart your runtime**. \n",
    "\n",
    "Just **uncomment the code in the next code cell** and run it!\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    "You may want to *switch your runtime* to have a GPU or TPU. To do so, go to *Runtime* > *Change runtime type* and select the desired hardware accelerator.\n",
    "\n",
    "Note that if you switch your runtime you have to follow the installation instructions again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install numpy==1.23.4\n",
    "# !pip install git+https://github.com/lnccbrown/hssm@main\n",
    "# !pip install git+https://github.com/brown-ccv/hddm-wfpt@main\n",
    "# !pip install numpyro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import arviz as az\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytensor\n",
    "\n",
    "import hssm\n",
    "import ssms.basic_simulators\n",
    "\n",
    "pytensor.config.floatX = \"float32\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-simulate some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.696936</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.541875</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.334992</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.321992</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.303991</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.652007</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.023025</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.569003</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.659999</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.053026</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response\n",
       "0    3.696936       1.0\n",
       "1    4.541875       1.0\n",
       "2    1.334992       1.0\n",
       "3    1.321992       1.0\n",
       "4    1.303991       1.0\n",
       "..        ...       ...\n",
       "995  1.652007       1.0\n",
       "996  2.023025      -1.0\n",
       "997  1.569003       1.0\n",
       "998  0.659999      -1.0\n",
       "999  2.053026       1.0\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simulate some data\n",
    "v_true, a_true, z_true, t_true = [0.5, 1.5, 0.5, 0.3]\n",
    "obs_angle = ssms.basic_simulators.simulator(\n",
    "    [v_true, a_true, z_true, t_true], model=\"ddm\", n_samples=1000\n",
    ")\n",
    "obs_angle = np.column_stack([obs_angle[\"rts\"][:, 0], obs_angle[\"choices\"][:, 0]])\n",
    "data = pd.DataFrame(obs_angle, columns=[\"rt\", \"response\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three Kinds of Likelihoods\n",
    "\n",
    "HSSM supports 3 kinds of likelihood functions supported via the `loglik_kind` parameter to the `HSSM` class:\n",
    "\n",
    "- `\"analytical\"`: These likelihoods are usually closed-form solutions to the actual likelihoods. For example, For `ddm` models, HSSM provides the analytical likelihoods in [Navarro & Fuss (2009)](https://psycnet.apa.org/record/2009-11068-003). HSSM expects these functions to be written with `pytensor`, which can be compiled by `pytensor` as part of a computational graph. As such, they are differentiable as well.\n",
    "- `\"approx_differentiable\"`: These likelihoods are usually approximations of the actual likelihood functions with neural networks. These networks can be trained with any popular deep learning framework such as `PyTorch` and `TensorFlow` and saved as `onnx` files. HSSM can load the `onnx` files and translate the information of the neural network with either the `jax` or the `pytensor` backends. Please see below for detailed explanations for these backends. The `backend` option can be supplied via the `\"backend\"` field in `model_config`. This field of `model_config` is not applicable to other kinds of likelihoods.\n",
    "\n",
    "  - the `jax` backend: The basic computations in the likelihood are jax operations (valid `JAX` functions), which are wrapped in a `pytensor` `Op`. When sampling using the default NUTS sampler in `PyMC`, this option might be slightly faster but more prone to compatibility issues especially during parallel sampling due how `JAX` handles paralellism.The preferred usage of this backend is together with the `nuts_numpyro` and `black_jax` (experimental) samplers. Here JAX support is native and performance is optimized.\n",
    "  - the `pytensor` backend: The basic computations in the likelihood are pytensor operations (valid `pytensor` functions). When sampling using the default NUTS sampler in `PyMC`, this option allows for maximum compatibility. Not recommended when using `JAX`-based samplers.\n",
    "\n",
    "- `\"blackbox\"`: Use this option for \"black box\" likelihoods that are not differentiable. These likelihoods are typically `Callable`s in Python that cannot be directly integrated to a `pytensor` computational graph. `hssm` will wrap these `Callable`s in a `pytensor` `Op` so it can be part of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default vs. Custom Likelihoods\n",
    "\n",
    "HSSM provides many default likelihood functions out-of-the-box. The supported likelihoods are:\n",
    "\n",
    "- For `analytical` kind: `ddm` and `ddm_sdv` models.\n",
    "- For `approx_differentiable` kind: `ddm`, `ddm_sdv`, `angle`, `levy`, `ornstein`, `weibull`, `race_no_bias_angle_4` and `ddm_seq2_no_bias`.\n",
    "- For `blackbox` kind: `ddm`, `ddm_sdv` and `full_ddm` models.\n",
    "\n",
    "For a model that has default likelihood functions, only the `model` argument needs to be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_analytical = hssm.HSSM(data, model=\"ddm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hierarchical Sequential Sampling Model\n",
       "Model: ddm\n",
       "\n",
       "Response variable: rt,response\n",
       "Likelihood: analytical\n",
       "Observations: 1000\n",
       "\n",
       "Parameters:\n",
       "\n",
       "v:\n",
       "    Prior: Normal(mu: 0.0, sigma: 2.0)\n",
       "    Explicit bounds: (-inf, inf)\n",
       "a:\n",
       "    Prior: HalfNormal(sigma: 2.0)\n",
       "    Explicit bounds: (0.0, inf)\n",
       "z:\n",
       "    Prior: Uniform(lower: 0.0, upper: 1.0)\n",
       "    Explicit bounds: (0.0, 1.0)\n",
       "t:\n",
       "    Prior: HalfNormal(sigma: 2.0, initval: 0.10000000149011612)\n",
       "    Explicit bounds: (0.0, inf)\n",
       "\n",
       "Lapse probability: 0.05\n",
       "Lapse distribution: Uniform(lower: 0.0, upper: 10.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_analytical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.0.3 (20230416.2022)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"470pt\" height=\"233pt\"\n",
       " viewBox=\"0.00 0.00 469.94 232.91\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 228.91)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-228.91 465.94,-228.91 465.94,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clusterrt,response_obs (1000) x rt,response_extra_dim_0 (2)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M109.69,-8C109.69,-8 395.69,-8 395.69,-8 401.69,-8 407.69,-14 407.69,-20 407.69,-20 407.69,-109.95 407.69,-109.95 407.69,-115.95 401.69,-121.95 395.69,-121.95 395.69,-121.95 109.69,-121.95 109.69,-121.95 103.69,-121.95 97.69,-115.95 97.69,-109.95 97.69,-109.95 97.69,-20 97.69,-20 97.69,-14 103.69,-8 109.69,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"252.69\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">rt,response_obs (1000) x rt,response_extra_dim_0 (2)</text>\n",
       "</g>\n",
       "<!-- a -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>a</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"58.69\" cy=\"-187.43\" rx=\"58.88\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"58.69\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">a</text>\n",
       "<text text-anchor=\"middle\" x=\"58.69\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"58.69\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
       "</g>\n",
       "<!-- rt,response -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>rt,response</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"252.69\" cy=\"-76.48\" rx=\"96.33\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"252.69\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">rt,response</text>\n",
       "<text text-anchor=\"middle\" x=\"252.69\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"252.69\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">SSMRandomVariable</text>\n",
       "</g>\n",
       "<!-- a&#45;&gt;rt,response -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>a&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M102.23,-161.98C127.82,-147.6 160.64,-129.18 189.09,-113.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"190.55,-115.83 197.55,-107.88 187.12,-109.73 190.55,-115.83\"/>\n",
       "</g>\n",
       "<!-- t -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>t</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"193.69\" cy=\"-187.43\" rx=\"58.88\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"193.69\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">t</text>\n",
       "<text text-anchor=\"middle\" x=\"193.69\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"193.69\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
       "</g>\n",
       "<!-- t&#45;&gt;rt,response -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>t&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M212.52,-151.66C217.48,-142.5 222.9,-132.49 228.11,-122.87\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"231.54,-124.88 233.23,-114.42 225.39,-121.55 231.54,-124.88\"/>\n",
       "</g>\n",
       "<!-- v -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>v</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"311.69\" cy=\"-187.43\" rx=\"41.94\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"311.69\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">v</text>\n",
       "<text text-anchor=\"middle\" x=\"311.69\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"311.69\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- v&#45;&gt;rt,response -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>v&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M293.67,-153.15C288.53,-143.66 282.84,-133.16 277.38,-123.08\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"280.08,-121.71 272.24,-114.59 273.93,-125.05 280.08,-121.71\"/>\n",
       "</g>\n",
       "<!-- z -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"416.69\" cy=\"-187.43\" rx=\"45.01\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"416.69\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">z</text>\n",
       "<text text-anchor=\"middle\" x=\"416.69\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"416.69\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- z&#45;&gt;rt,response -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>z&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M382.16,-163.02C375.7,-158.66 369,-154.16 362.69,-149.95 345.6,-138.55 326.96,-126.23 309.96,-115.03\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"312.51,-111.86 302.23,-109.29 308.66,-117.71 312.51,-111.86\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x2c6529940>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_analytical.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ddm` and `ddm_sdv` models have `analytical` and `approx_differentiable` likelihoods. If `loglik_kind` is not specified, the `analytical` likelihood will be used. We can however directly specify the `loglik_kind` argument for a given model, and if available, the likelihood backend will be switched automatically. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_approx_diff = hssm.HSSM(\n",
    "    data, model=\"ddm\", loglik_kind=\"approx_differentiable\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the model graph looks the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.0.3 (20230416.2022)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"426pt\" height=\"233pt\"\n",
       " viewBox=\"0.00 0.00 425.51 232.91\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 228.91)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-228.91 421.51,-228.91 421.51,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>clusterrt,response_obs (1000) x rt,response_extra_dim_0 (2)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M65.25,-8C65.25,-8 351.25,-8 351.25,-8 357.25,-8 363.25,-14 363.25,-20 363.25,-20 363.25,-109.95 363.25,-109.95 363.25,-115.95 357.25,-121.95 351.25,-121.95 351.25,-121.95 65.25,-121.95 65.25,-121.95 59.25,-121.95 53.25,-115.95 53.25,-109.95 53.25,-109.95 53.25,-20 53.25,-20 53.25,-14 59.25,-8 65.25,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"208.25\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">rt,response_obs (1000) x rt,response_extra_dim_0 (2)</text>\n",
       "</g>\n",
       "<!-- a -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>a</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"45.25\" cy=\"-187.43\" rx=\"45.01\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">a</text>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"45.25\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- rt,response -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>rt,response</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"208.25\" cy=\"-76.48\" rx=\"96.33\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"208.25\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">rt,response</text>\n",
       "<text text-anchor=\"middle\" x=\"208.25\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"208.25\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">SSMRandomVariable</text>\n",
       "</g>\n",
       "<!-- a&#45;&gt;rt,response -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>a&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M79.82,-163.32C100.62,-149.42 127.61,-131.38 151.47,-115.43\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"153.08,-117.9 159.44,-109.43 149.19,-112.08 153.08,-117.9\"/>\n",
       "</g>\n",
       "<!-- t -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>t</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"154.25\" cy=\"-187.43\" rx=\"45.01\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"154.25\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">t</text>\n",
       "<text text-anchor=\"middle\" x=\"154.25\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"154.25\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- t&#45;&gt;rt,response -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>t&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M171.19,-152.26C175.79,-142.99 180.83,-132.81 185.68,-123.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"189.14,-124.93 190.44,-114.41 182.87,-121.82 189.14,-124.93\"/>\n",
       "</g>\n",
       "<!-- v -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>v</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"263.25\" cy=\"-187.43\" rx=\"45.01\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"263.25\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">v</text>\n",
       "<text text-anchor=\"middle\" x=\"263.25\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"263.25\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- v&#45;&gt;rt,response -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>v&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M246.15,-152.55C241.47,-143.28 236.32,-133.07 231.36,-123.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"234.12,-121.95 226.49,-114.6 227.87,-125.1 234.12,-121.95\"/>\n",
       "</g>\n",
       "<!-- z -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"372.25\" cy=\"-187.43\" rx=\"45.01\" ry=\"37.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"372.25\" y=\"-198.73\" font-family=\"Times,serif\" font-size=\"14.00\">z</text>\n",
       "<text text-anchor=\"middle\" x=\"372.25\" y=\"-183.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"372.25\" y=\"-168.73\" font-family=\"Times,serif\" font-size=\"14.00\">Uniform</text>\n",
       "</g>\n",
       "<!-- z&#45;&gt;rt,response -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>z&#45;&gt;rt,response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M337.47,-163.32C316.46,-149.37 289.18,-131.24 265.1,-115.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"267.31,-111.85 257.05,-109.23 263.44,-117.68 267.31,-111.85\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x2c6779130>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_approx_diff.graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that the likelihood is now coming from a different backend by printing the model string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hierarchical Sequential Sampling Model\n",
       "Model: ddm\n",
       "\n",
       "Response variable: rt,response\n",
       "Likelihood: approx_differentiable\n",
       "Observations: 1000\n",
       "\n",
       "Parameters:\n",
       "\n",
       "v:\n",
       "    Prior: Uniform(lower: -3.0, upper: 3.0)\n",
       "    Explicit bounds: (-3.0, 3.0)\n",
       "a:\n",
       "    Prior: Uniform(lower: 0.30000001192092896, upper: 2.5)\n",
       "    Explicit bounds: (0.3, 2.5)\n",
       "z:\n",
       "    Prior: Uniform(lower: 0.0, upper: 1.0)\n",
       "    Explicit bounds: (0.0, 1.0)\n",
       "t:\n",
       "    Prior: Uniform(lower: 0.0, upper: 2.0)\n",
       "    Explicit bounds: (0.0, 2.0)\n",
       "\n",
       "Lapse probability: 0.05\n",
       "Lapse distribution: Uniform(lower: 0.0, upper: 10.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_approx_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how under the *Likelihood* rubric, it now says \"approx_differentiable\". Another simple way to check this is to access the `loglik_kind` attribute of our HSSM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'approx_differentiable'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_approx_diff.loglik_kind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overriding default likelihoods\n",
    "\n",
    "Sometimes a likelihood other than the default version is preferred. In that case, you can supply a likelihood function directly to the `loglik` parameter. We will discuss acceptable likelihood function types in a moment. \n",
    "\n",
    "For illustration we load the basic analytical DDM likelihood, which is shipped with HSSM and supply it manually our HSSM model class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hssm.likelihoods.analytical import logp_ddm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_analytical_override = hssm.HSSM(\n",
    "    data, model=\"ddm\", loglik_kind=\"analytical\", loglik=logp_ddm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HSSM automatically constructed our model with the likelihood function we provided. We can now take posterior samples as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 4 jobs)\n",
      "NUTS: [a, t, z, v]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [2000/2000 00:07&lt;00:00 Sampling 2 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 500 tune and 500 draw iterations (1_000 + 1_000 draws total) took 7 seconds.\n",
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior8d37df8f-3c85-4578-ae4f-bb943c2fc94b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior8d37df8f-3c85-4578-ae4f-bb943c2fc94b\" class = \"xr-section-summary\">posterior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 2, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    v        (chain, draw) float32 0.5014 0.5968 0.5473 ... 0.5746 0.5631 0.5481\n",
       "    a        (chain, draw) float32 1.43 1.41 1.482 1.421 ... 1.478 1.452 1.45\n",
       "    t        (chain, draw) float32 0.3178 0.3284 0.2889 ... 0.2959 0.3236 0.3242\n",
       "    z        (chain, draw) float32 0.5035 0.4724 0.4776 ... 0.4803 0.4859 0.4869\n",
       "Attributes:\n",
       "    created_at:                  2023-09-05T18:13:08.797619\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.6.1\n",
       "    sampling_time:               7.172336101531982\n",
       "    tuning_steps:                500\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.12.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5daa1a4f-388b-44bf-bb47-7e426208082e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5daa1a4f-388b-44bf-bb47-7e426208082e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6876712-eb72-463c-91dd-3fb6a9bf54f7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6876712-eb72-463c-91dd-3fb6a9bf54f7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-529f7fde-b78a-488b-b428-55640143a28f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-529f7fde-b78a-488b-b428-55640143a28f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57753491-7176-4c19-8641-6a58e9265c86' class='xr-var-data-in' type='checkbox'><label for='data-57753491-7176-4c19-8641-6a58e9265c86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-16610080-7b22-4417-afc6-b5bfe3325a0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16610080-7b22-4417-afc6-b5bfe3325a0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01350834-e938-44a8-b0a9-2cc9410c8eb1' class='xr-var-data-in' type='checkbox'><label for='data-01350834-e938-44a8-b0a9-2cc9410c8eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a253fd88-9ad2-4efd-9abd-f6a188cf8d88' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a253fd88-9ad2-4efd-9abd-f6a188cf8d88' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5014 0.5968 ... 0.5631 0.5481</div><input id='attrs-304c5141-2648-45be-a492-3d07cc4f2c28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-304c5141-2648-45be-a492-3d07cc4f2c28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f00440e-af80-48ed-a6cb-efbeab0cd4bc' class='xr-var-data-in' type='checkbox'><label for='data-1f00440e-af80-48ed-a6cb-efbeab0cd4bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.5013974 , 0.5968258 , 0.547282  , 0.61497504, 0.5968594 ,\n",
       "        0.5376522 , 0.5524871 , 0.55201954, 0.61084455, 0.55232465,\n",
       "        0.5329222 , 0.5244286 , 0.5748008 , 0.57386184, 0.58950645,\n",
       "        0.5135537 , 0.5116461 , 0.62833536, 0.62833536, 0.49469766,\n",
       "        0.45126906, 0.5959056 , 0.59181595, 0.5513842 , 0.57956755,\n",
       "        0.6290346 , 0.4867545 , 0.58977145, 0.56541854, 0.5981825 ,\n",
       "        0.5809083 , 0.55873823, 0.5981286 , 0.519772  , 0.5346469 ,\n",
       "        0.5089941 , 0.55569196, 0.58541393, 0.5589054 , 0.5335996 ,\n",
       "        0.56525755, 0.5578527 , 0.59271324, 0.5734941 , 0.60025305,\n",
       "        0.54637694, 0.5860329 , 0.5860329 , 0.58198   , 0.54642576,\n",
       "        0.6081347 , 0.54780686, 0.5864038 , 0.58175987, 0.5617084 ,\n",
       "        0.5617084 , 0.5701936 , 0.53642404, 0.5970527 , 0.53819555,\n",
       "        0.545003  , 0.5586501 , 0.5570437 , 0.5560903 , 0.5423847 ,\n",
       "        0.5692025 , 0.6066664 , 0.5590321 , 0.54234153, 0.60513794,\n",
       "        0.56940085, 0.4890655 , 0.5601525 , 0.5406598 , 0.56883115,\n",
       "        0.5666139 , 0.48666233, 0.49525562, 0.56091857, 0.54052806,\n",
       "        0.5564552 , 0.576722  , 0.54647326, 0.56445295, 0.50455797,\n",
       "        0.5916992 , 0.56999177, 0.5595669 , 0.6258129 , 0.6012697 ,\n",
       "        0.6073091 , 0.6088033 , 0.6249801 , 0.6032524 , 0.5831116 ,\n",
       "        0.57779664, 0.6047527 , 0.5941201 , 0.6229125 , 0.596771  ,\n",
       "...\n",
       "        0.5058365 , 0.577355  , 0.5367916 , 0.5512963 , 0.5722085 ,\n",
       "        0.57384855, 0.54316276, 0.5953291 , 0.58547276, 0.57741314,\n",
       "        0.57741314, 0.6316855 , 0.63964313, 0.6093611 , 0.5846943 ,\n",
       "        0.5668186 , 0.6027782 , 0.56973785, 0.541878  , 0.5302714 ,\n",
       "        0.5871308 , 0.5974495 , 0.53412986, 0.6216408 , 0.5854036 ,\n",
       "        0.513196  , 0.54265213, 0.5388068 , 0.54299265, 0.5650501 ,\n",
       "        0.573091  , 0.5800625 , 0.534014  , 0.6337936 , 0.6371205 ,\n",
       "        0.5432562 , 0.5660626 , 0.57577837, 0.54429585, 0.5831462 ,\n",
       "        0.5945086 , 0.5621216 , 0.6158967 , 0.6117093 , 0.6045588 ,\n",
       "        0.56564975, 0.57205945, 0.56359166, 0.57303226, 0.5780843 ,\n",
       "        0.5786037 , 0.5526615 , 0.60384285, 0.57289267, 0.58253175,\n",
       "        0.5905847 , 0.6093307 , 0.6277364 , 0.58973646, 0.5652865 ,\n",
       "        0.5983912 , 0.5751031 , 0.5909758 , 0.53609324, 0.5778004 ,\n",
       "        0.6177113 , 0.5679928 , 0.54584545, 0.5176116 , 0.5461395 ,\n",
       "        0.6193964 , 0.56110746, 0.55978745, 0.5392999 , 0.5975189 ,\n",
       "        0.57797533, 0.5732875 , 0.61078596, 0.61438704, 0.534107  ,\n",
       "        0.5749675 , 0.57249767, 0.55247146, 0.5951757 , 0.57512254,\n",
       "        0.56745267, 0.51969475, 0.55172926, 0.5607467 , 0.5240205 ,\n",
       "        0.58196694, 0.5915483 , 0.5745868 , 0.5630509 , 0.54812366]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.43 1.41 1.482 ... 1.452 1.45</div><input id='attrs-c9769dd9-14e1-41bc-899d-368d302fc893' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9769dd9-14e1-41bc-899d-368d302fc893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6844b8c-1903-4b38-99dc-6552f69243eb' class='xr-var-data-in' type='checkbox'><label for='data-d6844b8c-1903-4b38-99dc-6552f69243eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.4302939, 1.4096847, 1.4816191, 1.4209514, 1.4770273, 1.4231691,\n",
       "        1.4035803, 1.4441748, 1.4353732, 1.4777108, 1.4007832, 1.4418155,\n",
       "        1.4432441, 1.4509784, 1.4368088, 1.4256057, 1.425113 , 1.4549253,\n",
       "        1.4549253, 1.412872 , 1.3651905, 1.3911433, 1.3750023, 1.442521 ,\n",
       "        1.5042504, 1.4441017, 1.4286909, 1.4155769, 1.4355127, 1.4284755,\n",
       "        1.4301564, 1.414753 , 1.4408281, 1.4340128, 1.4217993, 1.4190482,\n",
       "        1.4655949, 1.3967526, 1.395811 , 1.4188164, 1.4519492, 1.4607818,\n",
       "        1.4166414, 1.4163779, 1.4072345, 1.4157151, 1.4777032, 1.4777032,\n",
       "        1.4636121, 1.4347342, 1.4800832, 1.4027683, 1.3736019, 1.3912506,\n",
       "        1.4009726, 1.4009726, 1.4065216, 1.4024384, 1.4530966, 1.4210231,\n",
       "        1.428105 , 1.4838731, 1.4320661, 1.4258168, 1.4243352, 1.4223273,\n",
       "        1.465437 , 1.4648638, 1.4404341, 1.3997259, 1.3917885, 1.4614564,\n",
       "        1.4868329, 1.4656069, 1.4183743, 1.4225596, 1.4205947, 1.4451249,\n",
       "        1.4575462, 1.4156913, 1.4507297, 1.4204991, 1.412666 , 1.4384313,\n",
       "        1.4639308, 1.4525598, 1.435613 , 1.459244 , 1.5053449, 1.4804504,\n",
       "        1.5092057, 1.5028137, 1.4753587, 1.4796224, 1.4648944, 1.4641569,\n",
       "        1.4814641, 1.4400873, 1.4851965, 1.5151335, 1.4185791, 1.4239032,\n",
       "        1.505216 , 1.4932827, 1.496833 , 1.5008773, 1.4243993, 1.4687444,\n",
       "        1.4467337, 1.448549 , 1.4577919, 1.3974737, 1.3894389, 1.3965235,\n",
       "        1.4610478, 1.4733825, 1.4854747, 1.437741 , 1.4676692, 1.4307029,\n",
       "...\n",
       "        1.4619465, 1.425944 , 1.4662981, 1.4336227, 1.4121745, 1.4509234,\n",
       "        1.3982285, 1.4162812, 1.4299557, 1.4943719, 1.4724193, 1.4300146,\n",
       "        1.5116975, 1.4124473, 1.3980589, 1.4508122, 1.4698671, 1.4199687,\n",
       "        1.4698573, 1.4367752, 1.4173803, 1.4424962, 1.4176817, 1.4439447,\n",
       "        1.4334518, 1.4418017, 1.4494835, 1.4424348, 1.4297897, 1.4896791,\n",
       "        1.4792432, 1.4792432, 1.4305972, 1.4498883, 1.4414943, 1.4242593,\n",
       "        1.4217807, 1.428677 , 1.3914689, 1.5039767, 1.4714079, 1.4576662,\n",
       "        1.476844 , 1.4553318, 1.4718739, 1.4300877, 1.4464097, 1.3892179,\n",
       "        1.4024471, 1.4047757, 1.4354944, 1.4467512, 1.4605702, 1.4745913,\n",
       "        1.488734 , 1.4524347, 1.425642 , 1.4318346, 1.439185 , 1.4593933,\n",
       "        1.4862844, 1.4624217, 1.4930288, 1.4148438, 1.3965291, 1.4171273,\n",
       "        1.5083501, 1.5052813, 1.4054991, 1.3827955, 1.4369848, 1.4538088,\n",
       "        1.4415983, 1.4531571, 1.4638104, 1.4390025, 1.474805 , 1.4674599,\n",
       "        1.4639701, 1.4825965, 1.4899348, 1.3955225, 1.4346278, 1.4268475,\n",
       "        1.4758939, 1.4864773, 1.4617077, 1.4204581, 1.4482707, 1.4480095,\n",
       "        1.4149348, 1.4809978, 1.4847186, 1.4443301, 1.4180465, 1.4667083,\n",
       "        1.4164075, 1.4175144, 1.4171321, 1.4054649, 1.4112177, 1.4304875,\n",
       "        1.4070412, 1.393383 , 1.394997 , 1.4085706, 1.4371315, 1.4557822,\n",
       "        1.4645051, 1.429279 , 1.4500923, 1.4341809, 1.436942 , 1.4780827,\n",
       "        1.451783 , 1.449669 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.3178 0.3284 ... 0.3236 0.3242</div><input id='attrs-580cffd7-1599-4624-8d61-0599b6fc64a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-580cffd7-1599-4624-8d61-0599b6fc64a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3036338-6797-4255-b665-97873e9b02a6' class='xr-var-data-in' type='checkbox'><label for='data-e3036338-6797-4255-b665-97873e9b02a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.31783795, 0.32844272, 0.28886926, 0.31727707, 0.32580572,\n",
       "        0.3091964 , 0.33474192, 0.31721315, 0.30642298, 0.279638  ,\n",
       "        0.30534798, 0.34571704, 0.29271466, 0.3243592 , 0.3067675 ,\n",
       "        0.3390062 , 0.33423796, 0.29779518, 0.29779518, 0.36322594,\n",
       "        0.35298306, 0.35466897, 0.3473541 , 0.32202414, 0.2627305 ,\n",
       "        0.2909716 , 0.32567778, 0.33767712, 0.29731357, 0.32663527,\n",
       "        0.30965737, 0.34290454, 0.2887184 , 0.33865854, 0.3580449 ,\n",
       "        0.33236462, 0.30952674, 0.34408587, 0.33644706, 0.3220758 ,\n",
       "        0.33298376, 0.32324648, 0.3276122 , 0.3464697 , 0.32634464,\n",
       "        0.32842267, 0.28917983, 0.28917983, 0.30155802, 0.3135743 ,\n",
       "        0.28052932, 0.34955618, 0.3487608 , 0.36750412, 0.35129243,\n",
       "        0.35129243, 0.32631698, 0.32943475, 0.33286452, 0.29585236,\n",
       "        0.30253726, 0.31722704, 0.32088283, 0.29751432, 0.35545725,\n",
       "        0.34263238, 0.26752186, 0.32314   , 0.3071192 , 0.32713547,\n",
       "        0.33765393, 0.31524196, 0.2996083 , 0.28805262, 0.32434756,\n",
       "        0.33957547, 0.3252339 , 0.31384152, 0.34509027, 0.31233573,\n",
       "        0.30424577, 0.32792267, 0.32586136, 0.32657644, 0.2903986 ,\n",
       "        0.32390028, 0.32079718, 0.29481477, 0.28103095, 0.26917374,\n",
       "        0.25969538, 0.28385398, 0.29669055, 0.29001305, 0.28654853,\n",
       "        0.32648405, 0.2932973 , 0.2779347 , 0.28144476, 0.28281295,\n",
       "...\n",
       "        0.320354  , 0.3375956 , 0.3163461 , 0.29400054, 0.2997427 ,\n",
       "        0.29896197, 0.3183687 , 0.32762328, 0.276571  , 0.27838403,\n",
       "        0.27838403, 0.31745788, 0.28540033, 0.31853133, 0.31484333,\n",
       "        0.31955063, 0.31237286, 0.32930538, 0.32043517, 0.27571565,\n",
       "        0.31282657, 0.32868314, 0.26580262, 0.2958254 , 0.33202627,\n",
       "        0.31891254, 0.35634384, 0.35195354, 0.34935233, 0.30267638,\n",
       "        0.31341866, 0.3217885 , 0.32000726, 0.28697592, 0.27238038,\n",
       "        0.33037367, 0.3229906 , 0.31820726, 0.2911721 , 0.27970746,\n",
       "        0.28558755, 0.28906628, 0.32578212, 0.30951375, 0.32095316,\n",
       "        0.26719427, 0.2893556 , 0.36432564, 0.32652777, 0.31657088,\n",
       "        0.3129926 , 0.338187  , 0.3027839 , 0.29233003, 0.31210458,\n",
       "        0.29354662, 0.29914603, 0.29203147, 0.3047752 , 0.27636462,\n",
       "        0.34673136, 0.3281205 , 0.34484756, 0.26544124, 0.2791464 ,\n",
       "        0.28888395, 0.33582136, 0.32347476, 0.33163956, 0.33991113,\n",
       "        0.30779508, 0.29762557, 0.2945326 , 0.33166945, 0.3004466 ,\n",
       "        0.30831367, 0.3106858 , 0.3326032 , 0.31497988, 0.32284817,\n",
       "        0.31537053, 0.31542495, 0.30685797, 0.3400538 , 0.3305983 ,\n",
       "        0.3187741 , 0.33232087, 0.30398458, 0.31986815, 0.3028714 ,\n",
       "        0.31517115, 0.30332065, 0.29585567, 0.32359704, 0.32417926]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5035 0.4724 ... 0.4859 0.4869</div><input id='attrs-e22bdeeb-3855-49e0-ba15-016953c3f175' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e22bdeeb-3855-49e0-ba15-016953c3f175' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac3fcaca-1deb-4b50-867a-88dfc7594dc1' class='xr-var-data-in' type='checkbox'><label for='data-ac3fcaca-1deb-4b50-867a-88dfc7594dc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.5034547 , 0.47241303, 0.47761884, 0.47861257, 0.48123193,\n",
       "        0.4975412 , 0.50624084, 0.47639915, 0.48158005, 0.46050936,\n",
       "        0.50288796, 0.503233  , 0.47906503, 0.46512797, 0.4725686 ,\n",
       "        0.49457294, 0.5151105 , 0.46094555, 0.46094555, 0.51708686,\n",
       "        0.5171758 , 0.47617364, 0.4806221 , 0.48663497, 0.48581553,\n",
       "        0.4617744 , 0.49728623, 0.4776286 , 0.47381765, 0.46590155,\n",
       "        0.48009315, 0.48534933, 0.47644657, 0.49488375, 0.50719863,\n",
       "        0.50571275, 0.49645334, 0.47854823, 0.48310304, 0.48113185,\n",
       "        0.49276704, 0.49776962, 0.4814843 , 0.48774692, 0.4743577 ,\n",
       "        0.49112284, 0.46414435, 0.46414435, 0.48597032, 0.48607057,\n",
       "        0.4722001 , 0.48779753, 0.48630214, 0.47616854, 0.47754398,\n",
       "        0.47754398, 0.48896116, 0.47403613, 0.47862273, 0.4884367 ,\n",
       "        0.4886095 , 0.49117017, 0.47334495, 0.48619714, 0.49211246,\n",
       "        0.49903396, 0.46099135, 0.49929884, 0.4927748 , 0.47506368,\n",
       "        0.46860495, 0.5057688 , 0.4963486 , 0.48891637, 0.48390633,\n",
       "        0.4869154 , 0.49300897, 0.51049125, 0.48674577, 0.48078695,\n",
       "        0.48964357, 0.474103  , 0.49261716, 0.48729154, 0.49593592,\n",
       "        0.4809664 , 0.4873819 , 0.48783547, 0.45614675, 0.477525  ,\n",
       "        0.47189757, 0.47433072, 0.4782178 , 0.4750597 , 0.4800372 ,\n",
       "        0.48568407, 0.482108  , 0.47171226, 0.4886552 , 0.48890856,\n",
       "...\n",
       "        0.49723893, 0.48436594, 0.49550065, 0.47924414, 0.4730763 ,\n",
       "        0.48068857, 0.49024916, 0.47552812, 0.47728887, 0.4731445 ,\n",
       "        0.4731445 , 0.46722463, 0.45508295, 0.47076905, 0.47854298,\n",
       "        0.4650419 , 0.4790489 , 0.4810082 , 0.49853718, 0.46442908,\n",
       "        0.48165712, 0.5061899 , 0.46956432, 0.46983528, 0.47362185,\n",
       "        0.50482774, 0.48936874, 0.49273273, 0.4922507 , 0.4683926 ,\n",
       "        0.48928457, 0.49692824, 0.48825753, 0.4499723 , 0.46136567,\n",
       "        0.49839067, 0.48542336, 0.48840898, 0.49572384, 0.46737087,\n",
       "        0.47375107, 0.48506212, 0.45883226, 0.44836444, 0.4539587 ,\n",
       "        0.47299725, 0.49150833, 0.47591645, 0.48635516, 0.46993655,\n",
       "        0.49118766, 0.49331278, 0.481184  , 0.48952842, 0.4685583 ,\n",
       "        0.47823298, 0.46531832, 0.46322784, 0.4548211 , 0.48110598,\n",
       "        0.47844627, 0.47532243, 0.50057536, 0.47296003, 0.47951323,\n",
       "        0.4576026 , 0.49039152, 0.50907606, 0.49732208, 0.48091048,\n",
       "        0.45805067, 0.47612795, 0.48923865, 0.5000964 , 0.47525156,\n",
       "        0.48240688, 0.46305317, 0.46543315, 0.47379833, 0.4967665 ,\n",
       "        0.47697908, 0.4625873 , 0.4740701 , 0.48555312, 0.48427168,\n",
       "        0.4751497 , 0.50694096, 0.4702201 , 0.47506487, 0.4934322 ,\n",
       "        0.47821236, 0.46597537, 0.48025927, 0.48589975, 0.4869439 ]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3824acb0-e674-48b1-830c-88bc684388de' class='xr-section-summary-in' type='checkbox'  ><label for='section-3824acb0-e674-48b1-830c-88bc684388de' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be8f6469-a7d3-4d32-966b-084bb8c83a28' class='xr-index-data-in' type='checkbox'/><label for='index-be8f6469-a7d3-4d32-966b-084bb8c83a28' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7964d2e5-fcb2-4be1-a34f-3af501644f5a' class='xr-index-data-in' type='checkbox'/><label for='index-7964d2e5-fcb2-4be1-a34f-3af501644f5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b44e0de2-55fd-483c-82f1-fb70066db000' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b44e0de2-55fd-483c-82f1-fb70066db000' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-05T18:13:08.797619</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.6.1</dd><dt><span>sampling_time :</span></dt><dd>7.172336101531982</dd><dt><span>tuning_steps :</span></dt><dd>500</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.12.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats520be0c2-2eef-459c-89a9-247c95391bbb\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats520be0c2-2eef-459c-89a9-247c95391bbb\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 2, draw: 500)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
       "Data variables: (12/17)\n",
       "    n_steps                (chain, draw) float64 3.0 7.0 7.0 ... 7.0 15.0 7.0\n",
       "    index_in_trajectory    (chain, draw) int64 -1 -4 4 -3 -2 ... -5 3 -2 -9 -1\n",
       "    tree_depth             (chain, draw) int64 2 3 3 3 3 3 3 3 ... 3 4 3 2 3 4 3\n",
       "    reached_max_treedepth  (chain, draw) bool False False False ... False False\n",
       "    step_size              (chain, draw) float64 0.5739 0.5739 ... 0.4254 0.4254\n",
       "    diverging              (chain, draw) bool False False False ... False False\n",
       "    ...                     ...\n",
       "    largest_eigval         (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "    perf_counter_start     (chain, draw) float64 21.75 21.76 ... 24.82 24.83\n",
       "    perf_counter_diff      (chain, draw) float64 0.003121 0.006499 ... 0.006251\n",
       "    process_time_diff      (chain, draw) float64 0.003121 0.006498 ... 0.006252\n",
       "    energy_error           (chain, draw) float64 -0.09022 -0.08921 ... 0.04615\n",
       "    energy                 (chain, draw) float64 1.964e+03 ... 1.962e+03\n",
       "Attributes:\n",
       "    created_at:                  2023-09-05T18:13:08.802527\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.6.1\n",
       "    sampling_time:               7.172336101531982\n",
       "    tuning_steps:                500\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.12.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-24031aae-33ed-461b-800c-6332e596f543' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-24031aae-33ed-461b-800c-6332e596f543' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77b3ba10-adec-48fc-9ae0-d1da722c3330' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77b3ba10-adec-48fc-9ae0-d1da722c3330' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-498f1fbb-1f20-4eae-859a-9bf08a3bf3cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-498f1fbb-1f20-4eae-859a-9bf08a3bf3cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6403bd60-4a8d-4ccc-87d7-d6511d89b86b' class='xr-var-data-in' type='checkbox'><label for='data-6403bd60-4a8d-4ccc-87d7-d6511d89b86b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-64ecc4e2-d508-4ef4-aaa3-2892fbfad673' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-64ecc4e2-d508-4ef4-aaa3-2892fbfad673' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd66de02-e76c-4715-a72f-28b466479d12' class='xr-var-data-in' type='checkbox'><label for='data-cd66de02-e76c-4715-a72f-28b466479d12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0265225a-a85f-4fda-ae97-dc048b19cdc4' class='xr-section-summary-in' type='checkbox'  ><label for='section-0265225a-a85f-4fda-ae97-dc048b19cdc4' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 7.0 7.0 7.0 ... 7.0 15.0 7.0</div><input id='attrs-51f6dcd5-ab7c-48d2-b67f-278c6c72280c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51f6dcd5-ab7c-48d2-b67f-278c6c72280c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e74808c3-95d7-40ce-a9cc-7c2847dfe316' class='xr-var-data-in' type='checkbox'><label for='data-e74808c3-95d7-40ce-a9cc-7c2847dfe316' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 11.,  7.,  7.,\n",
       "         7.,  3.,  7.,  3.,  7.,  3., 15.,  3.,  7.,  3.,  7.,  5.,  7.,\n",
       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,\n",
       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
       "         3.,  3.,  3.,  3.,  7.,  3.,  7.,  3.,  1.,  3.,  3.,  7.,  3.,\n",
       "         7.,  7., 15.,  3.,  7.,  3.,  7.,  7.,  3., 11.,  3.,  7.,  3.,\n",
       "         7.,  3.,  7.,  7.,  7.,  7.,  3.,  3.,  3.,  7.,  7.,  3.,  3.,\n",
       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  3.,\n",
       "         3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3., 15.,  7.,  3.,\n",
       "         7.,  3.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
       "         7.,  3.,  7.,  7.,  3.,  3.,  7.,  5.,  3.,  7.,  7.,  7., 11.,\n",
       "         3.,  7.,  3.,  7.,  3.,  7.,  3.,  7.,  3.,  7.,  7.,  3.,  3.,\n",
       "         3.,  7., 15.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,\n",
       "         3.,  3.,  7., 15.,  7.,  3.,  7.,  5.,  7.,  7.,  7.,  3.,  7.,\n",
       "         7.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  5.,  3.,  7.,\n",
       "         7.,  7.,  3.,  3.,  7.,  3., 11.,  7.,  3.,  3.,  7.,  3.,  7.,\n",
       "         7.,  3.,  7.,  7.,  7.,  3.,  3.,  3.,  7.,  7.,  7.,  7.,  3.,\n",
       "         3.,  7.,  7.,  7.,  3.,  3.,  1.,  1.,  7.,  7.,  7.,  7.,  7.,\n",
       "         7.,  3.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  3.,  3.,  3.,  3.,\n",
       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,\n",
       "...\n",
       "         7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  3.,  7.,  7.,\n",
       "         3.,  3.,  7.,  7.,  7.,  3.,  7.,  3.,  7., 11.,  7.,  3.,  7.,\n",
       "         7.,  7.,  3.,  3.,  7.,  7.,  3.,  7.,  7.,  3., 15.,  7.,  7.,\n",
       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
       "         7., 11.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,\n",
       "         3.,  7.,  3.,  7., 15.,  7.,  7.,  3.,  7., 15.,  7.,  7.,  7.,\n",
       "         7.,  3.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  3.,  3.,\n",
       "         3.,  3.,  7.,  7., 15.,  7.,  3.,  3.,  7.,  7.,  7.,  7.,  3.,\n",
       "         7.,  7., 11.,  3.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,  7., 15.,\n",
       "         3.,  7.,  7.,  7.,  3., 15.,  7.,  7.,  7.,  3.,  7., 15.,  7.,\n",
       "         7.,  3.,  3.,  3.,  3.,  3.,  7.,  3.,  3.,  3.,  7.,  7.,  7.,\n",
       "         7.,  3.,  7.,  3.,  3.,  7.,  3., 11.,  3.,  7.,  7.,  7., 15.,\n",
       "         7.,  7.,  3., 15.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  3.,\n",
       "         7.,  7.,  7.,  7.,  3.,  3.,  7.,  5.,  7.,  7.,  3., 11.,  3.,\n",
       "         7.,  7.,  7.,  3.,  1.,  7., 15.,  3.,  7.,  7.,  7.,  9.,  7.,\n",
       "         3.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
       "         7.,  7., 15.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,  3.,  3.,\n",
       "         7.,  3.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
       "         3.,  3.,  3., 11.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
       "        15.,  7.,  3.,  7., 15.,  7.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-1 -4 4 -3 -2 -2 ... -5 3 -2 -9 -1</div><input id='attrs-29bb6a4b-6cd4-4783-9f23-17ee4d05be3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29bb6a4b-6cd4-4783-9f23-17ee4d05be3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bae48088-ede1-4d99-96f0-8acd8b08b37f' class='xr-var-data-in' type='checkbox'><label for='data-bae48088-ede1-4d99-96f0-8acd8b08b37f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -1,  -4,   4,  -3,  -2,  -2,  -5,   3,   2,  -2,   6,  -3,  -3,\n",
       "         -1,   1,  -5,   1,   7,   0,  -5,  -1,  -2,  -1,   2,   2,   4,\n",
       "         -6,   4,   2,  -3,   2,  -2,   3,   6,  -1,  -1,   3,  -2,   2,\n",
       "         -2,   3,   1,  -6,   2,   2,  -3,  -3,   0,  -2,   4,   4,  -6,\n",
       "          2,  -1,  -1,   0,   3,   2,  -3,  -2,  -1,  -2,  -3,  -2,   3,\n",
       "         -1,  -6,  -4,   2,  -6,   2,  -5,   2,   2,   4,   2,  -4,   2,\n",
       "         -3,   3,  -2,  -7,  -3,   2,  -2,   3,   2,  -4,  -2,  -2,   1,\n",
       "        -10,   1,   5,  -3,   2,   2,   4,   4,  -1,   4,  -2,   3,  -2,\n",
       "          1,  -2,   4,  -3,   7,   2,  -1,   3,  -3,   2,  -4,   2,  -1,\n",
       "         -4,   3,  -3,  -2,  -2,  -1,   2,  -2,  -6,  -2,   6,   2,  -2,\n",
       "         -1,   1,  -2,   7,  -3,   3,   2,  -2,  -2,   2,   2,   2,  -6,\n",
       "         -2,   4,  -2,   2,   2,   2,   1,   2,   2,   6,  -3,   1,   3,\n",
       "         -2,   0,   3,   1,  -4,   3,   4,  -2,   3,   4,   0,  -1,   2,\n",
       "         -2,   0,   5,   1,  -1,   2,  -1,   3,  -3,  -6,  -5,  -1,   1,\n",
       "         -6,   2,  -5,  -5,  -1,   7,  -1,   1,  -3,   5,   2,  -2,   6,\n",
       "         -3,  -4,  -3,   2,  -1,   2,  -7,   6,  -1,  -2,  -1,  -1,  -6,\n",
       "          2,  -3,  -3,  -2,  -3,   1,   2,   1,  -3,   4,   1,  -2,  -2,\n",
       "          1,   2,   4,  -3,  -2,   2,   0,  -1,   2,  -2,   4,  -5,   2,\n",
       "          7,   2,   3,  -2,   2,  -7,   2,  -5,  -5,   3,   3,   1,  -3,\n",
       "          4,   2,   5,  -1,   2,  -4,   3,   6,  -2,  -2,  -2,  -6,   2,\n",
       "...\n",
       "         -3,   2,  -3,   1,   6,  -1,   3,   3,  -3,  -2,  -2,  -5,  -2,\n",
       "         -2,  -3,   7,   5,  -4,   3,  -4,   1,  -4,   2,   4,   2,  -2,\n",
       "          2,  -6,   2,   2,  -6,   2,   0,  -3,   2,   3, -11,   2,   2,\n",
       "          4,  -5,  -2,  -2,  -2,  -1,   1,  -2,  -1,  -2,   2,  -7,  -7,\n",
       "         -2,  -2,   2,   6,  -3,  -1,  -5,  -2,  -2,  -5,   3,   1,  -4,\n",
       "          3,   4,  -2,   7,   5,   4,  -5,   2,  -2,  -1,   5,  -5,   6,\n",
       "          4,   1,   6,  -4,  -4,   1,  -2,   2,  -3,   2,   5,  -2,   0,\n",
       "         -2,  -2,  -3,  -2,  -3,  -1,  -2,   3,   3,   2,   4,   3,  -3,\n",
       "         -2,   2,   1,  -2,   2,  -2,  -3,   2,   5,   1,   5,  -2,   6,\n",
       "          1,   4,  -5,  -3,  -1,   5,   4,  -3,   1,   0,  -3,   5,   5,\n",
       "         -1,   2,   1,   3,   2,   1,  -1,   2,  -2,  -3,   1,   3,   2,\n",
       "         -4,  -2,   2,  -2,  -2,  -4,   3,  -3,   3,   5,   2,  -3,  -8,\n",
       "         -2,   6,   2,  -5,   2,   3,   1,  -2,   3,  -2,  -5,  -1,   0,\n",
       "         -4,  -2,   2,  -2,  -3,  -2,  -3,  -3,   4,  -2,  -2,   3,  -3,\n",
       "         -3,  -6,   4,   1,   1,  -3,  -6,  -1,   1,  -5,  -2,   4,   5,\n",
       "          3,   2,  -4,  -2,  -3,   5,   2,  -2,   5,  -2,  -6,   2,  -2,\n",
       "          3,  -3,   6,  -5,   2,   4,  -1,  -2,   1,   4,  -3,   3,  -2,\n",
       "          4,   1,  -4,   3,   1,   2,  -5,   4,   5,  -2,  -4,  -8,   4,\n",
       "          2,   1,   2,   3,   3,  -2,  -2,  -3,  -2,  -4,   3,   4,  -6,\n",
       "         -9,  -5,   3,  -2,  -9,  -1]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 3 3 3 3 3 3 3 ... 3 3 4 3 2 3 4 3</div><input id='attrs-85155550-a018-4e80-a307-096549827551' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85155550-a018-4e80-a307-096549827551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11ee3736-6ee5-41d5-8d62-2abee844e74e' class='xr-var-data-in' type='checkbox'><label for='data-11ee3736-6ee5-41d5-8d62-2abee844e74e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 2, 3, 2, 3, 2, 4, 2, 3,\n",
       "        2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3,\n",
       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 1, 2, 2, 3, 2, 3,\n",
       "        3, 4, 2, 3, 2, 3, 3, 2, 4, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 2, 3,\n",
       "        3, 2, 2, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 2, 3, 3, 3, 3, 3,\n",
       "        2, 3, 3, 2, 4, 3, 2, 3, 2, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
       "        3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 4, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3,\n",
       "        2, 2, 2, 3, 4, 2, 3, 2, 3, 3, 3, 3, 3, 2, 2, 2, 2, 3, 4, 3, 2, 3,\n",
       "        3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2,\n",
       "        2, 3, 2, 4, 3, 2, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3,\n",
       "        2, 2, 3, 3, 3, 2, 2, 1, 1, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 3,\n",
       "        3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 2, 3, 3, 2,\n",
       "        3, 2, 1, 3, 3, 2, 3, 2, 2, 4, 2, 2, 3, 2, 3, 4, 2, 3, 2, 3, 3, 3,\n",
       "        3, 3, 2, 3, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 3, 3,\n",
       "        3, 2, 2, 3, 2, 3, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3,\n",
       "        2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3, 2, 3, 3, 3, 4,\n",
       "        2, 3, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 2, 1, 3, 2, 2,\n",
       "        3, 2, 3, 3, 3, 2, 2, 2, 3, 2, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3,\n",
       "        3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 3, 3, 2,\n",
       "        3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2,\n",
       "...\n",
       "        4, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3,\n",
       "        2, 2, 3, 3, 3, 2, 2, 2, 3, 2, 3, 3, 3, 2, 2, 4, 3, 2, 2, 2, 3, 4,\n",
       "        4, 3, 2, 2, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3,\n",
       "        3, 1, 3, 3, 3, 3, 3, 4, 2, 3, 2, 3, 3, 3, 3, 4, 2, 3, 2, 3, 2, 3,\n",
       "        3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 3, 1, 2, 2, 2,\n",
       "        2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 1, 3,\n",
       "        3, 3, 3, 3, 2, 4, 3, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 2,\n",
       "        3, 3, 3, 4, 2, 2, 3, 3, 3, 3, 4, 2, 3, 2, 3, 3, 4, 3, 2, 1, 2, 2,\n",
       "        3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3,\n",
       "        3, 2, 3, 2, 3, 4, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 4, 3, 3,\n",
       "        3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 4, 3, 3, 2, 3, 3, 3, 2,\n",
       "        3, 3, 2, 3, 2, 3, 2, 3, 4, 3, 3, 2, 3, 4, 3, 3, 3, 3, 2, 3, 3, 3,\n",
       "        2, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 4, 3, 2, 2, 3, 3, 3, 3, 2, 3,\n",
       "        3, 4, 2, 2, 3, 3, 3, 3, 2, 3, 3, 4, 2, 3, 3, 3, 2, 4, 3, 3, 3, 2,\n",
       "        3, 4, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2, 3,\n",
       "        2, 4, 2, 3, 3, 3, 4, 3, 3, 2, 4, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3,\n",
       "        3, 3, 2, 2, 3, 3, 3, 3, 2, 4, 2, 3, 3, 3, 2, 1, 3, 4, 2, 3, 3, 3,\n",
       "        4, 3, 2, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3,\n",
       "        2, 2, 3, 3, 2, 2, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3, 4, 3, 2, 2, 2,\n",
       "        4, 3, 2, 3, 3, 2, 3, 3, 3, 3, 4, 3, 2, 3, 4, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-9aa6776c-ed8f-4e20-a524-d716f0db7753' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9aa6776c-ed8f-4e20-a524-d716f0db7753' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b1bacd-ee7c-41c0-be39-08d3a8eee286' class='xr-var-data-in' type='checkbox'><label for='data-00b1bacd-ee7c-41c0-be39-08d3a8eee286' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "...\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5739 0.5739 ... 0.4254 0.4254</div><input id='attrs-a1d9da03-506a-4186-8f82-893659f3e848' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1d9da03-506a-4186-8f82-893659f3e848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225a9f48-2d70-44c3-9f5c-b5604365b07b' class='xr-var-data-in' type='checkbox'><label for='data-225a9f48-2d70-44c3-9f5c-b5604365b07b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "        0.57394306, 0.57394306, 0.57394306, 0.57394306, 0.57394306,\n",
       "...\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ,\n",
       "        0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 , 0.4254096 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-1ceb97c8-0966-47b9-8fb6-d4a630793760' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ceb97c8-0966-47b9-8fb6-d4a630793760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31802785-ca6e-49af-8f07-0f44c0407c75' class='xr-var-data-in' type='checkbox'><label for='data-31802785-ca6e-49af-8f07-0f44c0407c75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "...\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.963e+03 ... -1.962e+03</div><input id='attrs-b7a45fa6-699c-440f-aee3-4575d61e5f50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7a45fa6-699c-440f-aee3-4575d61e5f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-655054ee-fda3-4e07-8b30-91048a23a3f1' class='xr-var-data-in' type='checkbox'><label for='data-655054ee-fda3-4e07-8b30-91048a23a3f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1963.12221801, -1962.49166611, -1963.36417443, -1963.00452524,\n",
       "        -1964.07659999, -1962.90901713, -1964.14317457, -1962.04678195,\n",
       "        -1962.91854761, -1965.86104642, -1966.6833699 , -1963.4477572 ,\n",
       "        -1962.12060915, -1964.38236262, -1961.64785737, -1962.68808111,\n",
       "        -1963.98267039, -1963.14300544, -1963.14300544, -1965.67427877,\n",
       "        -1969.14315713, -1964.6607257 , -1964.888907  , -1961.36175232,\n",
       "        -1965.29772592, -1963.51264536, -1963.69196051, -1962.38249442,\n",
       "        -1962.07045669, -1962.85854032, -1961.5755545 , -1962.16219165,\n",
       "        -1963.06255747, -1962.65908611, -1964.11685757, -1962.82522896,\n",
       "        -1962.19803874, -1963.10281483, -1962.39247244, -1962.09752784,\n",
       "        -1962.30485799, -1962.30528934, -1962.04514559, -1962.53680632,\n",
       "        -1962.63968676, -1961.58149529, -1963.3853834 , -1963.3853834 ,\n",
       "        -1961.86290047, -1961.35143104, -1963.04514088, -1962.73799692,\n",
       "        -1965.07388927, -1966.53907976, -1963.7502451 , -1963.7502451 ,\n",
       "        -1962.30895449, -1963.364848  , -1963.15128645, -1963.64864719,\n",
       "        -1962.34067909, -1963.41518802, -1962.13173428, -1962.86657533,\n",
       "        -1963.84199432, -1962.76704686, -1964.19017336, -1962.64587507,\n",
       "        -1961.82714825, -1963.32960862, -1963.85021971, -1964.38988994,\n",
       "        -1963.27748677, -1962.76560317, -1961.46120599, -1961.90295524,\n",
       "        -1963.9505998 , -1964.15334964, -1964.84410443, -1962.20138154,\n",
       "...\n",
       "        -1963.05474844, -1962.19366676, -1962.9012194 , -1966.35128694,\n",
       "        -1966.57002423, -1961.61401219, -1966.36682306, -1965.95443617,\n",
       "        -1962.83670999, -1962.3374815 , -1962.76635176, -1963.61051751,\n",
       "        -1962.87401652, -1962.59111297, -1962.32673877, -1961.48339244,\n",
       "        -1962.72665152, -1963.75007952, -1965.98331948, -1965.23436261,\n",
       "        -1961.78868199, -1961.20845467, -1961.43560242, -1963.100677  ,\n",
       "        -1963.48479911, -1962.37680037, -1963.20411651, -1964.03603447,\n",
       "        -1966.56541738, -1964.63048025, -1965.16699623, -1964.27051543,\n",
       "        -1966.5116347 , -1964.42928699, -1961.9673427 , -1961.83935183,\n",
       "        -1962.23475761, -1962.17767557, -1962.51423473, -1961.96253731,\n",
       "        -1962.17195189, -1962.71036923, -1963.12129223, -1967.10962288,\n",
       "        -1963.42027845, -1963.73967441, -1961.98824568, -1964.25486009,\n",
       "        -1965.38131769, -1963.0263697 , -1963.40861766, -1961.7554012 ,\n",
       "        -1963.35871575, -1962.77109446, -1962.51606871, -1965.4856437 ,\n",
       "        -1963.29769116, -1962.50126282, -1962.09102446, -1961.96632702,\n",
       "        -1962.50772385, -1963.13108301, -1963.47135902, -1963.88039113,\n",
       "        -1962.38866353, -1961.36180397, -1963.59618424, -1964.44262428,\n",
       "        -1963.60051765, -1961.93423932, -1961.62520604, -1963.34347448,\n",
       "        -1963.4971348 , -1961.72556221, -1962.37398482, -1961.33344919,\n",
       "        -1962.16308701, -1962.20937846, -1961.65576217, -1961.7819996 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.7591 0.5791 ... 0.9827 0.9683</div><input id='attrs-fd98f22f-815e-4411-b02e-aa5863fb6ee3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd98f22f-815e-4411-b02e-aa5863fb6ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72432a7c-b146-4b91-8e90-a18ce4114e58' class='xr-var-data-in' type='checkbox'><label for='data-72432a7c-b146-4b91-8e90-a18ce4114e58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.75906496, 0.5790683 , 0.93576926, 0.83245276,\n",
       "        0.99971348, 0.92571322, 0.99758682, 0.86329012, 0.85324157,\n",
       "        0.805631  , 0.99570593, 1.        , 0.56984108, 1.        ,\n",
       "        0.82570332, 0.90875109, 0.96369272, 0.41175814, 0.87869617,\n",
       "        0.65025479, 0.92956934, 1.        , 0.88955823, 0.52910454,\n",
       "        1.        , 0.96061561, 0.99905015, 0.96890834, 0.8177528 ,\n",
       "        0.97705865, 0.97777276, 0.78455241, 0.99620769, 0.8665682 ,\n",
       "        0.96861126, 0.94793252, 0.76366353, 0.98138337, 0.57838022,\n",
       "        0.96389642, 1.        , 0.97318909, 0.91183887, 0.98746426,\n",
       "        1.        , 0.70945573, 0.46334701, 0.65072071, 0.96706987,\n",
       "        0.8796343 , 0.98174425, 0.7245897 , 0.70301831, 1.        ,\n",
       "        0.15734672, 0.76609075, 0.96305258, 0.86460466, 0.8580911 ,\n",
       "        1.        , 0.9506135 , 0.83388762, 0.91789061, 0.96158647,\n",
       "        0.99344542, 0.94461534, 0.97489792, 0.65058891, 0.68557505,\n",
       "        0.96779044, 0.9945303 , 0.78398555, 0.85056689, 0.91975901,\n",
       "        0.72118469, 0.81965263, 0.87035019, 0.50171094, 1.        ,\n",
       "        0.78750614, 0.9636656 , 0.97140777, 0.78397897, 0.75770871,\n",
       "        1.        , 0.40869167, 0.94410405, 0.47749815, 1.        ,\n",
       "        0.98372989, 0.9121831 , 0.92571621, 0.70825845, 0.93869798,\n",
       "        0.95197186, 0.75512053, 0.79336352, 0.84869743, 1.        ,\n",
       "...\n",
       "        1.        , 0.9973701 , 0.91293711, 0.88515899, 0.98340579,\n",
       "        1.        , 0.98664285, 0.98133039, 0.69477508, 1.        ,\n",
       "        0.9767124 , 0.9904668 , 0.83338882, 0.93808326, 0.98873407,\n",
       "        0.51899961, 0.91372011, 0.84364232, 1.        , 0.99375562,\n",
       "        0.77244777, 1.        , 0.7070973 , 0.6031417 , 0.84217566,\n",
       "        0.82969003, 0.64667148, 0.95462637, 1.        , 0.74134849,\n",
       "        0.98989144, 0.8964818 , 1.        , 1.        , 0.78582076,\n",
       "        0.98872529, 0.88983693, 0.65810414, 0.99965166, 1.        ,\n",
       "        0.99418795, 0.82384202, 0.90775992, 0.54070086, 0.85928286,\n",
       "        1.        , 0.96721405, 0.88409304, 0.84686441, 0.91352906,\n",
       "        0.96124737, 0.83675573, 0.72867961, 1.        , 0.92162058,\n",
       "        0.99616371, 0.80861362, 0.92613857, 0.94669763, 0.99228097,\n",
       "        1.        , 0.90684623, 0.95475718, 0.29973811, 1.        ,\n",
       "        0.96203373, 0.8795805 , 0.67863644, 0.97627186, 0.89192119,\n",
       "        0.99136272, 0.99749039, 0.72371288, 0.99225388, 0.99919669,\n",
       "        0.74352412, 0.93272921, 0.98834786, 0.98068014, 0.99871987,\n",
       "        0.6823619 , 0.83592121, 0.85764511, 0.64282266, 0.66330446,\n",
       "        0.89713309, 0.82581744, 0.73707392, 0.71880277, 0.58673148,\n",
       "        0.54334117, 0.64594024, 0.6878807 , 1.        , 0.9871319 ,\n",
       "        0.9799047 , 0.9295149 , 0.89599042, 0.98270595, 0.96828908]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.29 0.6733 ... -0.0924 -0.1202</div><input id='attrs-ef136966-96e9-4b18-82cf-45d96c7063c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef136966-96e9-4b18-82cf-45d96c7063c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9f26056-e417-4b29-b80f-7ddc2972a653' class='xr-var-data-in' type='checkbox'><label for='data-b9f26056-e417-4b29-b80f-7ddc2972a653' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.28996213,  0.67331754,  1.00052347, -0.38650779,  0.31071749,\n",
       "        -0.72036539, -0.45698331, -0.63353667,  0.41744209,  0.4950086 ,\n",
       "        -1.28831178, -1.75332434, -0.25667265,  1.09263809, -1.17245634,\n",
       "         0.3504155 ,  0.30025299, -0.18031004,  1.3455505 ,  0.37792144,\n",
       "         0.77627112,  0.30697873, -0.2638581 ,  0.40186328,  1.34205912,\n",
       "        -0.22647876, -0.19672748, -0.24623893,  0.14228216,  0.45960812,\n",
       "        -0.17209883, -0.08932043,  0.4782977 , -0.46313149,  0.38539967,\n",
       "        -0.25536112,  0.13737692,  0.56717055, -0.07682692,  1.07472402,\n",
       "         0.16079161, -0.25735781, -0.1171513 ,  0.24149419, -0.18246729,\n",
       "        -0.13919543,  0.53443099,  1.83872803,  1.11103128,  0.08991973,\n",
       "         0.18576601, -0.14060479,  0.48589075,  0.78730973, -0.74417463,\n",
       "         3.00943947,  0.53242957,  0.11748061,  0.40466645,  0.26008395,\n",
       "        -0.31391068, -0.26554654,  0.42496571, -0.271896  , -0.3838939 ,\n",
       "        -0.82439623,  0.18585961, -0.28396318,  0.79817852,  0.83279762,\n",
       "        -0.15970216, -0.25940032,  0.49323474,  0.29536537,  0.32800137,\n",
       "         0.60673586,  0.36797542,  0.49257554,  1.41813852, -1.29021678,\n",
       "         0.53232611,  0.07564557,  0.08018621,  0.51574203,  0.34029189,\n",
       "        -0.29099765,  2.65901876,  0.09560992,  2.91149829, -0.49013554,\n",
       "        -0.20010185,  0.31946993,  0.20469817,  0.7921231 ,  0.19177412,\n",
       "         0.17856213,  0.67853127,  0.46014051,  0.53935908, -1.0559632 ,\n",
       "...\n",
       "        -0.23216547, -0.11842226,  0.35827854, -0.42388617, -0.49229403,\n",
       "        -0.46822318, -0.1184733 ,  0.0719124 ,  0.62279902, -0.21209742,\n",
       "         0.07242317,  0.03981806,  0.24214106,  0.10652997,  0.03438214,\n",
       "         1.00205065,  0.19516583,  0.37249347, -0.1871305 , -0.07502197,\n",
       "         0.28652183, -0.2677071 ,  0.72776513,  0.77472856, -0.51984986,\n",
       "        -0.84106782,  0.69004753, -0.59274972, -0.41347817,  0.51653164,\n",
       "        -0.14981892,  0.16926132, -0.15094865, -0.04523382,  0.42555236,\n",
       "        -0.13339143,  0.20610459,  0.83874853, -0.49349158, -0.51475764,\n",
       "        -0.54696142,  0.40810853,  0.13111895,  0.99571425,  0.56156419,\n",
       "        -0.23652637,  0.09398744,  0.29156135,  0.26243559, -0.3825277 ,\n",
       "        -0.25550899,  0.55783249,  0.91315318, -1.27290643, -0.67832131,\n",
       "        -0.14247069,  0.43911772,  0.28363838,  0.19393992, -0.16350307,\n",
       "        -0.07027349,  0.18824362,  0.14586827,  1.51029044, -1.40906408,\n",
       "         0.18303384,  0.40640846,  0.53312694, -0.25588211, -0.26465363,\n",
       "        -0.02666648, -0.10991814,  0.42596959, -0.29938351, -0.2248047 ,\n",
       "         0.70511299, -0.65944111, -0.47438709, -0.27293477, -0.09887086,\n",
       "         0.66698024,  0.33308139,  0.33015004,  0.57073918,  1.16216682,\n",
       "         0.28829743,  0.26845264,  0.72123831,  0.65334897,  1.15489511,\n",
       "         0.98986015,  0.90489025,  0.67307561, -0.53536379, -0.08926496,\n",
       "        -0.09084314,  0.10556341,  0.20079179, -0.09239608, -0.12019216]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-4871e4b2-5ccb-437f-bcdc-f091c7803419' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4871e4b2-5ccb-437f-bcdc-f091c7803419' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c094fd44-80c7-41e8-892b-750f203d974a' class='xr-var-data-in' type='checkbox'><label for='data-c094fd44-80c7-41e8-892b-750f203d974a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "...\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5865 0.5865 ... 0.5592 0.5592</div><input id='attrs-a98c8b0e-b9a2-4029-9237-e059976a8d97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a98c8b0e-b9a2-4029-9237-e059976a8d97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fbe22c-0a35-4c7f-9952-57d651e078bb' class='xr-var-data-in' type='checkbox'><label for='data-15fbe22c-0a35-4c7f-9952-57d651e078bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "        0.58647045, 0.58647045, 0.58647045, 0.58647045, 0.58647045,\n",
       "...\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ,\n",
       "        0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 , 0.5591756 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-636e96c1-5752-47e8-9686-688e4893abf5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-636e96c1-5752-47e8-9686-688e4893abf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3905e3c8-45c1-4699-bb6c-39658298085a' class='xr-var-data-in' type='checkbox'><label for='data-3905e3c8-45c1-4699-bb6c-39658298085a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "...\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.75 21.76 21.76 ... 24.82 24.83</div><input id='attrs-55e31042-119e-474f-afa1-e051f098a94b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55e31042-119e-474f-afa1-e051f098a94b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b437fa1-859d-4d87-8cf4-42e4086bf3bc' class='xr-var-data-in' type='checkbox'><label for='data-7b437fa1-859d-4d87-8cf4-42e4086bf3bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[21.75300142, 21.75619725, 21.76275683, 21.76941633, 21.77605592,\n",
       "        21.78280004, 21.78940508, 21.79599017, 21.80262083, 21.80907608,\n",
       "        21.81575525, 21.82581737, 21.83227367, 21.83883133, 21.84543912,\n",
       "        21.84884696, 21.855314  , 21.85856521, 21.86504354, 21.86843871,\n",
       "        21.88147979, 21.88475725, 21.89143562, 21.89481154, 21.90122363,\n",
       "        21.90602592, 21.91294063, 21.91959667, 21.92614558, 21.93274408,\n",
       "        21.93940125, 21.94279796, 21.94944204, 21.9560255 , 21.96263067,\n",
       "        21.96599246, 21.97261862, 21.97916354, 21.98573354, 21.98908867,\n",
       "        21.99556221, 22.00202542, 22.00848387, 22.01491633, 22.02143558,\n",
       "        22.02782571, 22.0343955 , 22.04107954, 22.04776162, 22.05425538,\n",
       "        22.06068696, 22.06734492, 22.07379908, 22.07702458, 22.08033267,\n",
       "        22.08365662, 22.08698125, 22.09356454, 22.097075  , 22.10372433,\n",
       "        22.10701867, 22.10864379, 22.11185067, 22.11511038, 22.12161442,\n",
       "        22.12481375, 22.13119854, 22.1377075 , 22.15067367, 22.15392008,\n",
       "        22.16037608, 22.16380408, 22.17036179, 22.17685488, 22.18010137,\n",
       "        22.18987117, 22.19311846, 22.19954804, 22.20274125, 22.20958333,\n",
       "        22.21288867, 22.21957904, 22.22633783, 22.23333012, 22.23985858,\n",
       "        22.24304875, 22.24626317, 22.249569  , 22.25599983, 22.26254221,\n",
       "        22.2659995 , 22.26941813, 22.28248875, 22.28921317, 22.29590604,\n",
       "        22.30259854, 22.30919075, 22.31585871, 22.32251529, 22.32918367,\n",
       "...\n",
       "        24.21006942, 24.21652054, 24.22297771, 24.23619938, 24.242992  ,\n",
       "        24.24955392, 24.25272846, 24.26563358, 24.27194813, 24.27847417,\n",
       "        24.28511983, 24.28846321, 24.29499979, 24.30143704, 24.30837396,\n",
       "        24.31173425, 24.31503162, 24.32190046, 24.32864162, 24.3353285 ,\n",
       "        24.34194383, 24.34537808, 24.34879783, 24.35543279, 24.36049442,\n",
       "        24.36717637, 24.37385912, 24.37710321, 24.38693004, 24.39039083,\n",
       "        24.39711879, 24.40360475, 24.40992925, 24.41312092, 24.41473333,\n",
       "        24.42124892, 24.43426129, 24.43753525, 24.44394358, 24.45078204,\n",
       "        24.45738692, 24.46566804, 24.47196304, 24.47507496, 24.48128354,\n",
       "        24.48757392, 24.49405212, 24.50049767, 24.50703721, 24.51035121,\n",
       "        24.51363083, 24.52017683, 24.52659687, 24.53284425, 24.53911871,\n",
       "        24.54549904, 24.55178183, 24.55798163, 24.5705385 , 24.57697517,\n",
       "        24.58342996, 24.58988275, 24.59641408, 24.59975796, 24.60305775,\n",
       "        24.60953879, 24.61588683, 24.61914683, 24.62230358, 24.62861692,\n",
       "        24.63191013, 24.63842437, 24.65105   , 24.65735625, 24.663573  ,\n",
       "        24.66976071, 24.67624587, 24.682792  , 24.68928842, 24.69568942,\n",
       "        24.7088935 , 24.71576825, 24.71911071, 24.72267538, 24.72609521,\n",
       "        24.73557979, 24.74207087, 24.74543258, 24.75193767, 24.75832683,\n",
       "        24.76156108, 24.76796562, 24.77439446, 24.78068496, 24.78719671,\n",
       "        24.7997755 , 24.806051  , 24.80942779, 24.81589142, 24.828453  ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003121 0.006499 ... 0.006251</div><input id='attrs-fd0600db-985d-4dc0-90d9-c9e6894b708f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd0600db-985d-4dc0-90d9-c9e6894b708f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe783a97-15f8-4a44-81ba-8aae7861fd8c' class='xr-var-data-in' type='checkbox'><label for='data-fe783a97-15f8-4a44-81ba-8aae7861fd8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00312096, 0.00649921, 0.00659921, 0.00657196, 0.00663313,\n",
       "        0.00651312, 0.00651913, 0.00656763, 0.00638979, 0.00660858,\n",
       "        0.00997775, 0.0063865 , 0.00649504, 0.00654633, 0.00334396,\n",
       "        0.00640137, 0.00319108, 0.00641854, 0.00333554, 0.01298013,\n",
       "        0.00321704, 0.00661608, 0.0033155 , 0.00635008, 0.00473921,\n",
       "        0.00682967, 0.00659154, 0.00648421, 0.00653704, 0.00659517,\n",
       "        0.00333596, 0.00658279, 0.00650754, 0.00653725, 0.00326554,\n",
       "        0.00655925, 0.00647658, 0.00650463, 0.00329338, 0.00640729,\n",
       "        0.00639129, 0.00639913, 0.00637183, 0.00645642, 0.00633067,\n",
       "        0.00650454, 0.00662367, 0.00661708, 0.00642558, 0.00636942,\n",
       "        0.00659787, 0.006392  , 0.00316617, 0.00324917, 0.00325517,\n",
       "        0.00326304, 0.0064915 , 0.00343133, 0.00657887, 0.00322879,\n",
       "        0.00156504, 0.00314158, 0.003196  , 0.00644362, 0.00313646,\n",
       "        0.00632079, 0.00644237, 0.01290229, 0.00318663, 0.00639296,\n",
       "        0.00336479, 0.00649258, 0.00642921, 0.00318596, 0.00970379,\n",
       "        0.00318271, 0.00636475, 0.00313408, 0.00674475, 0.00323733,\n",
       "        0.00660371, 0.00668104, 0.006895  , 0.00645833, 0.00313   ,\n",
       "        0.00315092, 0.00324113, 0.00637196, 0.00647746, 0.00339562,\n",
       "        0.00335971, 0.01300492, 0.00666108, 0.00663225, 0.00662663,\n",
       "        0.00653038, 0.00660563, 0.00659454, 0.00660608, 0.00324421,\n",
       "...\n",
       "        0.00638954, 0.00639729, 0.01315783, 0.00673362, 0.00649975,\n",
       "        0.00311437, 0.01284504, 0.00625129, 0.00646687, 0.00658717,\n",
       "        0.00328396, 0.00647346, 0.00637692, 0.00685221, 0.00329675,\n",
       "        0.00323746, 0.00678917, 0.00667713, 0.00662696, 0.00654696,\n",
       "        0.00337421, 0.00335692, 0.00657392, 0.00498554, 0.00657233,\n",
       "        0.00661508, 0.00318217, 0.00976171, 0.00339442, 0.00666458,\n",
       "        0.00642254, 0.00626404, 0.00312892, 0.001555  , 0.00645583,\n",
       "        0.01294675, 0.00321233, 0.00634717, 0.00677025, 0.00654567,\n",
       "        0.00817617, 0.00622371, 0.00304917, 0.00615038, 0.00622763,\n",
       "        0.006418  , 0.00638167, 0.00648167, 0.00325717, 0.00322254,\n",
       "        0.00648296, 0.00636017, 0.00618413, 0.00621113, 0.00632267,\n",
       "        0.00622496, 0.00613979, 0.01249325, 0.00636429, 0.00637946,\n",
       "        0.00639154, 0.00646787, 0.00328379, 0.00323913, 0.00642133,\n",
       "        0.00628504, 0.00320242, 0.00309646, 0.00625596, 0.00323621,\n",
       "        0.00645383, 0.01256163, 0.0062485 , 0.0061505 , 0.00612392,\n",
       "        0.00642146, 0.00648513, 0.00642221, 0.00633562, 0.01310979,\n",
       "        0.00677463, 0.00327   , 0.00347204, 0.00335237, 0.00942192,\n",
       "        0.00642575, 0.00329421, 0.006441  , 0.00632796, 0.00317067,\n",
       "        0.00634288, 0.00636542, 0.00622038, 0.00645083, 0.01251725,\n",
       "        0.00621388, 0.00330388, 0.00640217, 0.01249679, 0.00625142]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003121 0.006498 ... 0.006252</div><input id='attrs-4cbe3974-7304-4a9f-a3cf-9681ba36db33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cbe3974-7304-4a9f-a3cf-9681ba36db33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-961b4f33-8717-4a95-93b7-0863239b973c' class='xr-var-data-in' type='checkbox'><label for='data-961b4f33-8717-4a95-93b7-0863239b973c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.003121, 0.006498, 0.006599, 0.006572, 0.006616, 0.006484,\n",
       "        0.006519, 0.006567, 0.00639 , 0.006609, 0.009975, 0.006387,\n",
       "        0.006495, 0.006547, 0.003344, 0.006403, 0.003191, 0.006419,\n",
       "        0.003336, 0.01298 , 0.003217, 0.006617, 0.003316, 0.00635 ,\n",
       "        0.004739, 0.006826, 0.006592, 0.006484, 0.006537, 0.006594,\n",
       "        0.003335, 0.006582, 0.006508, 0.006538, 0.003269, 0.00656 ,\n",
       "        0.006477, 0.006504, 0.003295, 0.006408, 0.006391, 0.006399,\n",
       "        0.006372, 0.006456, 0.006332, 0.006504, 0.006624, 0.006617,\n",
       "        0.006426, 0.006369, 0.006597, 0.006392, 0.003166, 0.003249,\n",
       "        0.003256, 0.003264, 0.006491, 0.003431, 0.006579, 0.003228,\n",
       "        0.001565, 0.003142, 0.003196, 0.006443, 0.003136, 0.006321,\n",
       "        0.006443, 0.012902, 0.003187, 0.006393, 0.003365, 0.006492,\n",
       "        0.006429, 0.003185, 0.009705, 0.003182, 0.006365, 0.003134,\n",
       "        0.00674 , 0.003237, 0.006592, 0.006682, 0.006894, 0.006458,\n",
       "        0.00313 , 0.003151, 0.003241, 0.006371, 0.006478, 0.003395,\n",
       "        0.00336 , 0.013005, 0.006661, 0.006632, 0.006626, 0.00653 ,\n",
       "        0.006606, 0.006594, 0.006607, 0.003244, 0.006551, 0.003347,\n",
       "        0.00647 , 0.003196, 0.003166, 0.006411, 0.006407, 0.006586,\n",
       "        0.006535, 0.006402, 0.003123, 0.006298, 0.006254, 0.003205,\n",
       "        0.012844, 0.006512, 0.003217, 0.006478, 0.003328, 0.003223,\n",
       "...\n",
       "        0.003143, 0.003149, 0.003122, 0.006303, 0.006345, 0.006435,\n",
       "        0.006544, 0.003361, 0.00667 , 0.003323, 0.003283, 0.006619,\n",
       "        0.003287, 0.009841, 0.003178, 0.006374, 0.00639 , 0.006398,\n",
       "        0.013157, 0.006734, 0.0065  , 0.003115, 0.012845, 0.006251,\n",
       "        0.006467, 0.006587, 0.003284, 0.006473, 0.006377, 0.006831,\n",
       "        0.003296, 0.003238, 0.006759, 0.006678, 0.006626, 0.006548,\n",
       "        0.003374, 0.003357, 0.006574, 0.004981, 0.006573, 0.006615,\n",
       "        0.003182, 0.009761, 0.003394, 0.006665, 0.006423, 0.006264,\n",
       "        0.003128, 0.001556, 0.006456, 0.012939, 0.003212, 0.006348,\n",
       "        0.006763, 0.006546, 0.008177, 0.006224, 0.003049, 0.006151,\n",
       "        0.006229, 0.006418, 0.006382, 0.006483, 0.003258, 0.003223,\n",
       "        0.006483, 0.006361, 0.006184, 0.006211, 0.006322, 0.006225,\n",
       "        0.00614 , 0.012489, 0.006364, 0.006383, 0.006391, 0.006469,\n",
       "        0.003284, 0.003239, 0.006422, 0.006285, 0.003202, 0.003096,\n",
       "        0.006256, 0.003237, 0.006454, 0.012561, 0.006248, 0.006151,\n",
       "        0.006124, 0.006422, 0.006485, 0.006419, 0.006336, 0.013092,\n",
       "        0.006757, 0.00327 , 0.003462, 0.003352, 0.009423, 0.006426,\n",
       "        0.003294, 0.006441, 0.006328, 0.003171, 0.006343, 0.006366,\n",
       "        0.00622 , 0.006451, 0.012518, 0.006214, 0.003304, 0.006403,\n",
       "        0.012497, 0.006252]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.09022 -0.08921 ... 0.04615</div><input id='attrs-ec9996e2-1092-4757-8955-7ec29af0418c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec9996e2-1092-4757-8955-7ec29af0418c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1341cbc7-1691-4c05-ac1f-618564612f7d' class='xr-var-data-in' type='checkbox'><label for='data-1341cbc7-1691-4c05-ac1f-618564612f7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-9.02224976e-02, -8.92123825e-02,  3.19306875e-01,\n",
       "         9.68395982e-02,  2.47677313e-01, -2.30566854e-01,\n",
       "         1.68230409e-01, -4.84488262e-01,  2.45468838e-01,\n",
       "         4.95008601e-01,  5.39469996e-01, -1.75332434e+00,\n",
       "        -1.50377069e-01,  7.47804084e-01, -1.17245634e+00,\n",
       "         2.12934157e-01,  1.44818989e-02, -1.34206189e-01,\n",
       "         0.00000000e+00,  3.15312714e-01,  2.65433403e-01,\n",
       "        -2.16387291e-01, -5.23977916e-02, -3.60482925e-01,\n",
       "         5.54280476e-01, -6.86285020e-02,  1.16839771e-01,\n",
       "        -2.38732252e-01,  5.58751650e-02,  8.55457355e-02,\n",
       "        -1.72098832e-01,  2.94450621e-02,  4.78297698e-01,\n",
       "        -1.83328812e-01,  7.22297873e-02, -2.31551303e-01,\n",
       "         8.61740865e-03,  2.17070542e-02, -7.68269164e-02,\n",
       "         1.30640908e-01,  6.28053106e-02, -1.07565951e-01,\n",
       "        -7.98891717e-03,  3.05146164e-02, -3.35771285e-02,\n",
       "        -1.39195428e-01,  4.45738817e-01,  0.00000000e+00,\n",
       "        -2.80899349e-01, -7.35605412e-02,  8.13916254e-02,\n",
       "         3.09241690e-02,  3.18480515e-01,  7.87309729e-01,\n",
       "        -7.44174631e-01,  0.00000000e+00, -2.07018369e-01,\n",
       "         1.17480611e-01,  1.18706095e-01,  2.60083946e-01,\n",
       "...\n",
       "        -4.67701605e-02,  6.39704380e-02,  3.01760667e-01,\n",
       "        -2.58364978e-02, -1.26899868e-01,  9.39874442e-02,\n",
       "         2.82444481e-02,  2.62435585e-01, -1.00518597e-01,\n",
       "        -4.85696543e-02, -8.89451518e-02,  9.13153177e-01,\n",
       "        -4.80240781e-01, -6.78321308e-01, -4.00845931e-02,\n",
       "         1.21384150e-01, -4.40196797e-02,  4.18478097e-02,\n",
       "        -1.12930231e-01, -5.81748031e-02,  8.91993043e-02,\n",
       "        -6.50964478e-02,  1.51029044e+00, -1.40906408e+00,\n",
       "        -2.15883904e-02, -1.29944184e-02,  1.85958720e-01,\n",
       "        -3.84121855e-02, -2.64653627e-01,  2.49581031e-02,\n",
       "        -7.83710227e-02,  2.72157822e-01, -2.35513650e-02,\n",
       "         5.63907282e-03,  7.05112993e-01, -4.53559892e-01,\n",
       "        -1.33989208e-01, -2.72934765e-01, -5.69986689e-02,\n",
       "         3.11910843e-01, -1.86304608e-01, -6.07950707e-02,\n",
       "         3.52859028e-01, -3.49685202e-01, -2.29108487e-01,\n",
       "         2.68452637e-01,  3.70589459e-01, -6.21527486e-01,\n",
       "        -2.26127596e-01,  1.37638691e-01,  5.52587900e-02,\n",
       "         4.47979709e-01, -5.14122358e-01, -2.10571212e-02,\n",
       "        -9.08431402e-02,  5.96340796e-02,  2.27577486e-02,\n",
       "         1.20172509e-02,  4.61527448e-02]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.964e+03 1.965e+03 ... 1.962e+03</div><input id='attrs-9f609602-e9bf-4abc-8c0d-07baafe4d28a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f609602-e9bf-4abc-8c0d-07baafe4d28a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3e8dcad-9e20-4f1b-9f72-1afa7499f2d9' class='xr-var-data-in' type='checkbox'><label for='data-b3e8dcad-9e20-4f1b-9f72-1afa7499f2d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1964.40319097, 1964.53794793, 1965.78103834, 1964.26505881,\n",
       "        1965.83514408, 1965.10819845, 1968.34738628, 1963.97631138,\n",
       "        1963.23106364, 1966.71686923, 1968.56965056, 1966.08424707,\n",
       "        1964.10051117, 1966.1437154 , 1963.3842687 , 1963.71513268,\n",
       "        1965.40742226, 1965.60642797, 1966.59853393, 1965.92727867,\n",
       "        1972.41929782, 1973.91660033, 1965.47790901, 1965.87090664,\n",
       "        1968.59395028, 1966.09483683, 1964.52588582, 1964.1588138 ,\n",
       "        1963.44179065, 1964.77064791, 1962.99446763, 1962.74757137,\n",
       "        1964.25260902, 1963.52705413, 1965.60287027, 1964.89355305,\n",
       "        1964.22038452, 1966.00261394, 1963.51350074, 1964.36864981,\n",
       "        1962.66842978, 1962.94514108, 1962.77419229, 1963.54000399,\n",
       "        1963.82873243, 1963.16567708, 1964.95404247, 1968.36955765,\n",
       "        1964.91081235, 1962.34172198, 1963.70156475, 1964.22005998,\n",
       "        1965.87201394, 1967.31075499, 1966.56116987, 1967.84559155,\n",
       "        1966.26926351, 1963.7473533 , 1964.71867535, 1964.93738807,\n",
       "        1963.48910646, 1963.80574141, 1964.83649841, 1963.40783086,\n",
       "        1964.32399986, 1963.85631817, 1966.33743641, 1965.24941215,\n",
       "        1963.89047557, 1967.43930596, 1964.8239548 , 1965.84554814,\n",
       "        1966.71548888, 1964.20540486, 1964.03252748, 1962.73325473,\n",
       "        1964.75451053, 1966.54515696, 1969.4483501 , 1963.89306507,\n",
       "...\n",
       "        1964.26489988, 1963.15698845, 1965.18441061, 1967.77610856,\n",
       "        1970.08620284, 1968.45136352, 1967.04032872, 1968.89958583,\n",
       "        1966.41412259, 1965.04604173, 1964.12985486, 1964.95130444,\n",
       "        1963.8699727 , 1962.92650815, 1965.0440089 , 1962.73062273,\n",
       "        1963.23605349, 1966.30484737, 1966.85509308, 1966.20650638,\n",
       "        1965.80660058, 1963.00352341, 1961.89728075, 1965.23918413,\n",
       "        1966.9037561 , 1963.39836128, 1963.79035418, 1965.29768098,\n",
       "        1967.31750396, 1967.92805644, 1968.14509247, 1967.68447159,\n",
       "        1967.04886412, 1966.61024669, 1965.51338776, 1962.36911041,\n",
       "        1964.65700883, 1963.23015543, 1964.77306123, 1963.4315138 ,\n",
       "        1962.53600234, 1964.55412519, 1963.64301204, 1968.35683267,\n",
       "        1967.19960227, 1966.42371901, 1964.94460459, 1965.69353347,\n",
       "        1967.17928941, 1967.3652407 , 1964.38668309, 1964.90554721,\n",
       "        1967.90428279, 1963.78085341, 1963.66562857, 1966.70648392,\n",
       "        1967.00120145, 1963.44513223, 1963.54833812, 1962.56960464,\n",
       "        1965.32184748, 1964.52180267, 1966.4404645 , 1965.21406651,\n",
       "        1967.36778664, 1963.43828102, 1963.6972713 , 1966.95887012,\n",
       "        1968.25370683, 1965.48994772, 1967.81193452, 1965.36700711,\n",
       "        1967.29465591, 1963.55655467, 1963.65252436, 1963.53659494,\n",
       "        1962.50723773, 1963.55555914, 1963.93206356, 1962.19706346]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c05ab0c-09a5-407a-a1d6-bf7ad6b7eef9' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c05ab0c-09a5-407a-a1d6-bf7ad6b7eef9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81898992-ff1d-4307-acd8-db8f3590b982' class='xr-index-data-in' type='checkbox'/><label for='index-81898992-ff1d-4307-acd8-db8f3590b982' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ec0a410-f7e9-47bd-8148-c83d796f6a21' class='xr-index-data-in' type='checkbox'/><label for='index-3ec0a410-f7e9-47bd-8148-c83d796f6a21' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fd7ded9-69f5-43c1-8d52-0b2766d202d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8fd7ded9-69f5-43c1-8d52-0b2766d202d0' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-05T18:13:08.802527</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.6.1</dd><dt><span>sampling_time :</span></dt><dd>7.172336101531982</dd><dt><span>tuning_steps :</span></dt><dd>500</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.12.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data88c02f01-3c10-446a-b00e-4766a0d82a1a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data88c02f01-3c10-446a-b00e-4766a0d82a1a\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (rt,response_obs: 1000, rt,response_extra_dim_0: 2)\n",
       "Coordinates:\n",
       "  * rt,response_obs          (rt,response_obs) int64 0 1 2 3 ... 996 997 998 999\n",
       "  * rt,response_extra_dim_0  (rt,response_extra_dim_0) int64 0 1\n",
       "Data variables:\n",
       "    rt,response              (rt,response_obs, rt,response_extra_dim_0) float32 ...\n",
       "Attributes:\n",
       "    created_at:                  2023-09-05T18:13:08.805682\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.6.1\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.12.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-13fae142-4ae4-4f64-b3b9-729cf1c6f6cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13fae142-4ae4-4f64-b3b9-729cf1c6f6cd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>rt,response_obs</span>: 1000</li><li><span class='xr-has-index'>rt,response_extra_dim_0</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fb145a33-daeb-4b4b-9bb3-c6a977979d44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb145a33-daeb-4b4b-9bb3-c6a977979d44' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rt,response_obs</span></div><div class='xr-var-dims'>(rt,response_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-e36369da-13cc-4c12-bc0e-39bc6756bee4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e36369da-13cc-4c12-bc0e-39bc6756bee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5e7fcc9-ffcb-4861-900a-52bc170c8a1d' class='xr-var-data-in' type='checkbox'><label for='data-f5e7fcc9-ffcb-4861-900a-52bc170c8a1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rt,response_extra_dim_0</span></div><div class='xr-var-dims'>(rt,response_extra_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-3d1b2ff4-d826-4d51-9918-c0c78e46c90f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d1b2ff4-d826-4d51-9918-c0c78e46c90f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-276c6689-0b19-47ea-9839-6050e02008f1' class='xr-var-data-in' type='checkbox'><label for='data-276c6689-0b19-47ea-9839-6050e02008f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86f8193a-d8aa-4579-b938-fa01943267d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-86f8193a-d8aa-4579-b938-fa01943267d4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rt,response</span></div><div class='xr-var-dims'>(rt,response_obs, rt,response_extra_dim_0)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.697 1.0 4.542 ... -1.0 2.053 1.0</div><input id='attrs-ef098f75-b09d-4e96-afd7-0b22a289ff1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef098f75-b09d-4e96-afd7-0b22a289ff1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a77aeddb-e32c-4b1f-991c-82dc60f72db6' class='xr-var-data-in' type='checkbox'><label for='data-a77aeddb-e32c-4b1f-991c-82dc60f72db6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3.6969361,  1.       ],\n",
       "       [ 4.5418754,  1.       ],\n",
       "       [ 1.3349924,  1.       ],\n",
       "       ...,\n",
       "       [ 1.5690033,  1.       ],\n",
       "       [ 0.6599989, -1.       ],\n",
       "       [ 2.053026 ,  1.       ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf4f3320-1c17-4287-a774-fcd09c05617f' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf4f3320-1c17-4287-a774-fcd09c05617f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>rt,response_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-291883e7-9ede-4643-8b90-cf7d41798036' class='xr-index-data-in' type='checkbox'/><label for='index-291883e7-9ede-4643-8b90-cf7d41798036' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;rt,response_obs&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>rt,response_extra_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5aa9919d-1746-4c16-90ed-b65495f3fc76' class='xr-index-data-in' type='checkbox'/><label for='index-5aa9919d-1746-4c16-90ed-b65495f3fc76' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;rt,response_extra_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19f3acfc-dfc9-4743-9754-6f80738c4d42' class='xr-section-summary-in' type='checkbox'  checked><label for='section-19f3acfc-dfc9-4743-9754-6f80738c4d42' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-05T18:13:08.805682</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.6.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.12.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_analytical_override.sample(draws=500, tune=500, chains=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Custom Likelihoods\n",
    "\n",
    "If you are specifying a model with a kind of likelihood that's not included in the list above, then HSSM considers that you are using a custom model with custom likelihoods. In this case, you will need to specify your entire model. Below is the procedure to specify a custom model:\n",
    "\n",
    "1. Specify a `model` string. It can be any string that helps identify the model, but if it is not one of the model strings supported in the `ssm_simulators` package [see full list here](https://github.com/AlexanderFengler/ssm-simulators/blob/main/ssms/config/config.py), you will need to supply a `RandomVariable` class to `model_config` detailed below. Otherwise, you can still perform MCMC sampling, but sampling from the posterior predictive distribution will raise a ValueError.\n",
    "\n",
    "2. Specify a `model_config`. It typically contains the following fields:\n",
    "\n",
    "   - `\"list_params\"`: Required if your `model` string is not one of `ddm`, `ddm_sdv`, `full_ddm`, `angle`, `levy`, `ornstein`, `weibull`, `race_no_bias_angle_4` and `ddm_seq2_no_bias`. A list of `str` indicating the parameters of the model.\n",
    "     The order in which the parameters are specified in this list is important.\n",
    "     Values for each parameter will be passed to the likelihood function in this\n",
    "     order.\n",
    "   - `\"backend\"`: Optional. Only used when `loglik_kind` is `approx_differentiable` and\n",
    "     an onnx file is supplied for the likelihood approximation network (LAN).\n",
    "     Valid values are `\"jax\"` or `\"pytensor\"`. It determines whether the LAN in\n",
    "     ONNX should be converted to `\"jax\"` or `\"pytensor\"`. If not provided,\n",
    "     `jax` will be used for maximum performance.\n",
    "   - `\"default_priors\"`: Optional. A `dict` indicating the default priors for each parameter.\n",
    "   - `\"bounds\"`: Optional. A `dict` of `(lower, upper)` tuples indicating the acceptable boundaries for each parameter. In the case\n",
    "     of LAN, these bounds are training boundaries.\n",
    "   - `\"rv\"`: Optional. Can be a `RandomVariable` class containing the user's own\n",
    "     `rng_fn` function for sampling from the distribution that the user is\n",
    "     supplying. If not supplied, HSSM will automatically generate a\n",
    "     `RandomVariable` using the simulator identified by `model` from the\n",
    "     `ssm_simulators` package. If `model` is not supported in `ssm_simulators`,\n",
    "     a warning will be raised letting the user know that sampling from the\n",
    "     `RandomVariable` will result in errors.\n",
    "<br> </br>     \n",
    "\n",
    "3. Specify `loglik` and `loglik_kind`.\n",
    "\n",
    "4. Specify parameter priors in `include`.\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    " `default_priors` and `bounds` in `model_config` specifies  **default** priors and bounds for the model. Actual priors and defaults should be provided via the `include` list and will override these defaults.\n",
    "\n",
    "Below are a few examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# An angle model with an analytical likelihood function.\n",
    "# Because `model` is known, no `list_params` needs to be provided.\n",
    "\n",
    "custom_angle_model = hssm.HSSM(\n",
    "    data,\n",
    "    model=\"angle\",\n",
    "    model_config={\n",
    "        \"bounds\": {\n",
    "            \"v\": (-3.0, 3.0),\n",
    "            \"a\": (0.3, 3.0),\n",
    "            \"z\": (0.1, 0.9),\n",
    "            \"t\": (0.001, 2.0),\n",
    "            \"theta\": (-0.1, 1.3),\n",
    "        }  # bounds will be used to create Uniform (uninformative) priors by default\n",
    "        # if priors are not supplied in `include`.\n",
    "    },\n",
    "    loglik=custom_angle_logp,\n",
    "    loglik_kind=\"analytical\",\n",
    ")\n",
    "\n",
    "# A fully customized model with a custom likelihood function.\n",
    "# Because `model` is not known, a `list_params` needs to be provided.\n",
    "\n",
    "my_custom_model = hssm.HSSM(\n",
    "    data,\n",
    "    model=\"my_model\",\n",
    "    model_config={\n",
    "        \"list_params\": [\"v\", \"a\", \"z\", \"t\", \"theta\"],\n",
    "        \"bounds\": {\n",
    "            \"v\": (-3.0, 3.0),\n",
    "            \"a\": (0.3, 3.0),\n",
    "            \"z\": (0.1, 0.9),\n",
    "            \"t\": (0.001, 2.0),\n",
    "            \"theta\": (-0.1, 1.3),\n",
    "        } # bounds will be used to create Uniform (uninformative) priors by default\n",
    "          # if priors are not supplied in `include`.\n",
    "        \"default_priors\": ... # usually no need to supply this.\n",
    "        \"rv\": MyRV # provide a RandomVariable class if pps is needed.\n",
    "    },\n",
    "    loglik=\"my_model.onnx\", # Can be a path to an onnx model.\n",
    "    loglik_kind=\"approx_differentiable\",\n",
    "    include=[...]\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supported types of likelihoods\n",
    "\n",
    "When default likelihoods are not used, custom likelihoods are supplied via `loglik` argument to `HSSM`. Depending on what `loglik_kind` is used, `loglik` supports different types of Python objects:\n",
    "\n",
    "- `Type[pm.Distribution]`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can pass any **subclass** of `pm.Distribution` to `loglik` representing the underlying top-level distribution of the model. It has to be a class instead of an instance of the class.\n",
    "\n",
    "- `Op`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can pass a `pytensor` `Op` (an instance instead of the class itself), in which case HSSM will create a top-level `pm.Distirbution`, which calls this `Op` in its `logp` function to compute the log-likelihood.\n",
    "\n",
    "- `Callable`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can use any Python Callable as well. When `loglik_kind` is `blackbox`, HSSM will wrap it in a `pytensor` `Op` and create a top-level `pm.Distribution` with it. Otherwise, HSSM will assume that this Python callable is created with `pytensor` and is thus differentiable.\n",
    "\n",
    "- `str` or `Pathlike`: Only supported when `loglik_kind` is `approx_differentiable`.\n",
    "\n",
    "  The `str` or `Pathlike` indicates the path to an `onnx` file which represents the neural network for likelihood approximation. In the case of `str`, if the path indicated by `str` is not found locally, HSSM will also look for the `onnx` file in the official HuggingFace repo. An error is thrown when the `onnx` file is not found.\n",
    "\n",
    "**Note**\n",
    "\n",
    "When using `Op` and `Callable` types of likelihoods, they need to have the this signature:\n",
    "\n",
    "```\n",
    "def logp_fn(data, *):\n",
    "    ...\n",
    "```\n",
    "\n",
    "where `data` is a 2-column numpy array and `*` represents named arguments in the order of the parameters in `list_params`. For example, if a model's `list_params` is `[\"v\", \"a\", \"z\", \"t\"]`, then the `Op` or `Callable` should at least look like this:\n",
    "\n",
    "```\n",
    "def logp_fn(data, v, a, z, t):\n",
    "    ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `blackbox` likelihoods\n",
    "\n",
    "HSSM also supports \"black box\" likelihood functions, which are assumed to not be differentiable. When `loglik_kind` is `blackbox`, by default, HSSM will switch to a MCMC sampler that does not use differentiation. Below is an example showing how to use a `blackbox` likelihood function. We use a log-likelihood function for `ddm` written in Cython to show that you can use any function or computation inside this function as long as the function itself has the signature defined above. [See here](https://github.com/brown-ccv/hddm-wfpt/blob/9107e4f1e480afcce2cd3cb7ac2279f8aecb596c/hddm_wfpt/wfpt.pyx#L32-L52) for the function definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Slice: [a]\n",
      ">Slice: [t]\n",
      ">Slice: [z]\n",
      ">Slice: [v]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:06&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9x/cjrfyjd9443d4_0wt9qw8fhh0000gq/T/ipykernel_7387/517673889.py:11: RuntimeWarning: divide by zero encountered in log\n",
      "  return hddm_wfpt.wfpt.pdf_array(\n",
      "/var/folders/9x/cjrfyjd9443d4_0wt9qw8fhh0000gq/T/ipykernel_7387/517673889.py:11: RuntimeWarning: divide by zero encountered in log\n",
      "  return hddm_wfpt.wfpt.pdf_array(\n",
      "/var/folders/9x/cjrfyjd9443d4_0wt9qw8fhh0000gq/T/ipykernel_7387/517673889.py:11: RuntimeWarning: divide by zero encountered in log\n",
      "  return hddm_wfpt.wfpt.pdf_array(\n",
      "/var/folders/9x/cjrfyjd9443d4_0wt9qw8fhh0000gq/T/ipykernel_7387/517673889.py:11: RuntimeWarning: divide by zero encountered in log\n",
      "  return hddm_wfpt.wfpt.pdf_array(\n",
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 6 seconds.\n"
     ]
    }
   ],
   "source": [
    "import hddm_wfpt\n",
    "import bambi as bmb\n",
    "\n",
    "\n",
    "# Define a function with fun(data, *) signature\n",
    "def my_blackbox_loglik(data, v, a, z, t, err=1e-8):\n",
    "    data = data[:, 0] * data[:, 1]\n",
    "\n",
    "    # Our function expects inputs as float64, but they are not guaranteed to\n",
    "    # come in as such --> we type convert\n",
    "    return hddm_wfpt.wfpt.pdf_array(\n",
    "        np.float64(data),\n",
    "        np.float64(v),\n",
    "        0,\n",
    "        np.float64(2 * a),\n",
    "        np.float64(z),\n",
    "        0,\n",
    "        np.float64(t),\n",
    "        0,\n",
    "        err,\n",
    "        1,\n",
    "    )\n",
    "\n",
    "\n",
    "# Create the model with pdf_ddm_blackbox\n",
    "model = hssm.HSSM(\n",
    "    data=data,\n",
    "    model=\"ddm\",\n",
    "    loglik=my_blackbox_loglik,\n",
    "    loglik_kind=\"blackbox\",\n",
    "    model_config={\n",
    "        \"bounds\": {\n",
    "            \"v\": (-10.0, 10.0),\n",
    "            \"a\": (0.0, 4.0),\n",
    "            \"z\": (0.0, 1.0),\n",
    "            \"t\": (0.0, 2.0),\n",
    "        }\n",
    "    },\n",
    "    t=bmb.Prior(\"Uniform\", lower=0.0, upper=2.0, initval=0.1),\n",
    ")\n",
    "\n",
    "sample = model.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.442</td>\n",
       "      <td>0.028</td>\n",
       "      <td>1.387</td>\n",
       "      <td>1.493</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>2285.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.313</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1143.0</td>\n",
       "      <td>1795.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.483</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.567</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>2274.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "a  1.442  0.028   1.387    1.493      0.001    0.001    1537.0    2285.0   \n",
       "t  0.313  0.021   0.274    0.354      0.001    0.000    1143.0    1795.0   \n",
       "z  0.483  0.014   0.458    0.510      0.000    0.000    1190.0    1931.0   \n",
       "v  0.567  0.035   0.505    0.636      0.001    0.001    1333.0    2274.0   \n",
       "\n",
       "   r_hat  \n",
       "a   1.00  \n",
       "t   1.01  \n",
       "z   1.00  \n",
       "v   1.00  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'a'}>, <Axes: title={'center': 'a'}>],\n",
       "       [<Axes: title={'center': 't'}>, <Axes: title={'center': 't'}>],\n",
       "       [<Axes: title={'center': 'z'}>, <Axes: title={'center': 'z'}>],\n",
       "       [<Axes: title={'center': 'v'}>, <Axes: title={'center': 'v'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the blackbox interface provides maximum flexibility on the user side. We hope you will find it useful!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
